MARINE ECOSYSTEM RESTORATION WITH A FOCUS ON CORAL REEF ECOSYSTEMS by DIVYA ALICE VARKEY B.F.Sc., Kerala Agricultural University, 2003 M.F.Sc., Central Institute of Fisheries Education, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2010 © Divya Alice Varkey Abstract The declines of fish populations in ecosystems around the globe have triggered considerable interest in marine ecosystem restoration. In addition to focusing on individual fish populations, there is increased emphasis on understanding inter-species interactions and on understanding the human relationships with the ecosystems. My thesis approaches marine restoration from (a) practical aspects of considering multispecies interactions in the ecosystem (Ecopath with Ecosim models), estimating unreported and illegal catches (influence tables) and policy that considers the concerns of multiple stakeholders (Bayesian influence diagram modeling); (b) theoretical aspects of carrying capacity and fish life history analyzed using life history parameters (Population dynamics modeling). I begin my thesis by exploring the technological, socio-economic, and political history of Raja Ampat in Eastern Indonesia (my geographical focus) to understand resource management challenges and to calculate the trends in relative misreporting of fisheries catch. The unreported fish catch exceeds the reported fish catch by a factor of 1.5. My next chapter explores the ecological benefits of establishing marine protected areas for coral reef ecosystems in Raja Ampat using Ecopath, Ecosim and Ecospace models. I estimate an ideal minimum size of no-take areas— the size of no-take area at which the biomass density of reef fish reached an asymptote—to be 16 to 25 km2. Analysis of biomass density of reef fish in MPAs led to questions about ecosystem carrying capacity. To explore carrying capacity, I reconstruct ancient snapper population biomass using archaeological data obtained from fish middens using equilibrium age structure model. The results show that the ancient snapper population was about 2 to 4 times higher than the modern population biomass. To model the differing utilities of different stakeholders, in the next chapter, I develop a bayesian influence diagram model. The results indicate that restricting net fisheries and implementing 25% fisheries closure are robust scenarios favored under several combinations of the modeled variables and utility functions. The final chapter explores how the life history parameters of fish species affect the population response to restoration. It is expected that slow growing species would show a greater response to protection than fast growing species. ii Table of Contents Abstract ............................................................................................................................... ii Table of Contents ............................................................................................................... iii List of Tables ..................................................................................................................... ix List of Figures ..................................................................................................................... x Acknowledgements ........................................................................................................... xii Dedication ........................................................................................................................ xiv Co-Authorship Statement.................................................................................................. xv 1 Introduction ................................................................................................................. 1 1.1 Rationale: brief history of fisheries management ................................................ 1 1.2 Context ................................................................................................................. 4 1.2.1 Brief description of study area - Raja Ampat Islands ................................... 5 1.2.2 Historical and political background of marine resource use in Raja Ampat 6 1.2.3 Current management in Raja Ampat............................................................. 8 1.3 1.3.1 Restoration within an EBM framework ...................................................... 10 1.3.2 Coral reefs ................................................................................................... 11 1.3.3 Ecopath with Ecosim .................................................................................. 12 1.3.4 Thesis outline .............................................................................................. 13 1.4 2 Thesis focus: restoration in fisheries with focus on coral reef ecosystems ........ 10 References .......................................................................................................... 18 Illegal, Unreported and Unregulated Fisheries Catch in Raja Ampat Regency, Eastern Indonesia .............................................................................................................. 29 2.1 Introduction ........................................................................................................ 29 2.1.1 The IUU problem in Indonesia ................................................................... 29 2.1.2 Study area—Raja Ampat Archipelago........................................................ 30 iii 2.2 2.2.1 Catch reconstruction ................................................................................... 31 2.2.2 Compilation of the influence table .............................................................. 32 2.2.3 Quantifying incentive.................................................................................. 33 2.2.4 Anchor points .............................................................................................. 34 2.2.5 Addressing uncertainty ............................................................................... 36 2.2.6 Quantifying IUU catch revenues in Raja Ampat Regency 2003-2006 ....... 37 2.3 Results ................................................................................................................ 38 2.3.1 Catch reconstruction and IUU catch estimation ......................................... 38 2.3.2 Quantifying the IUU catch revenues in Raja Ampat Regency 2003-2006 . 39 2.4 3 Methods .............................................................................................................. 30 Discussion .......................................................................................................... 41 2.4.1 Catch reconstruction ................................................................................... 41 2.4.2 Estimation of IUU fishing in Raja Ampat .................................................. 41 2.4.3 Quantifying the economics of IUU catch in Raja Ampat 2003-2006 ......... 48 2.5 Conclusion.......................................................................................................... 49 2.6 References .......................................................................................................... 50 Ecological Restoration and Ideal Minimum Size of No-Take Zones in Marine Protected Areas of Raja Ampat, Indonesia ....................................................................... 56 3.1 Introduction ........................................................................................................ 56 3.1.1 MPA for ecosystem based management ..................................................... 56 3.1.2 Raja Ampat ................................................................................................. 57 3.1.3 Birds Head Seascape ecosystem based management project and spatial ecosystem based management research interests ...................................................... 58 3.2 Methods .............................................................................................................. 59 3.2.1 Ecopath with Ecosim and Ecospace ........................................................... 59 3.2.2 Ecospace models used in the analysis ......................................................... 60 iv 3.2.3 Ecosystem effects of restricting fisheries inside the MPAs ........................ 61 3.2.4 Ecological benefits of single large versus several small MPAs ................. 62 3.3 Results ................................................................................................................ 64 3.3.1 Ecosystem effects of restricting fisheries inside the MPAs (Research question-1) ................................................................................................................ 64 3.3.2 3.4 4 Ecological benefits of single large versus several small MPAs ................. 68 Discussion .......................................................................................................... 70 3.4.1 Ecosystem effects of restricting fisheries inside the MPAs ........................ 70 3.4.2 Ecological benefits of single large versus several small MPAs ................. 72 3.5 Conclusion.......................................................................................................... 74 3.6 References .......................................................................................................... 75 Reconstructing Ancient New Zealand Snapper Biomass from Archaeological Data81 4.1 Introduction ........................................................................................................ 81 4.1.1 Fisheries management and restoration ........................................................ 81 4.1.2 Modern snapper fishing in New Zealand .................................................... 82 4.1.3 Prehistoric fishing in New Zealand............................................................. 83 4.2 Methods .............................................................................................................. 84 4.2.1 Growth parameters of the modern population ............................................ 84 4.2.2 Midden descriptions/archaeological data .................................................... 86 4.2.3 Growth parameters of the ancient population ............................................. 87 4.2.4 Candidate growth parameters of the ancient population............................. 88 4.2.5 Estimation of mortality for ancient population ........................................... 89 4.2.6 Proportion of large fish in the population ................................................... 92 4.2.7 Ancient population size............................................................................... 93 4.3 Results ................................................................................................................ 97 v 4.3.1 Growth parameters of the ancient population ............................................. 97 4.3.2 Comparison of modern and ancient population biomass ............................ 98 4.4 5 Discussion ........................................................................................................ 100 4.4.1 Ancient growth parameters ....................................................................... 100 4.4.2 Ancient population biomass ...................................................................... 101 4.5 Conclusion........................................................................................................ 103 4.6 References ........................................................................................................ 104 Evaluation of Restoration Goals for Raja Ampat Coral Reef Ecosystem Using Influence Diagram Modeling .......................................................................................... 108 5.1 Introduction ...................................................................................................... 108 5.1.1 Need for marine restoration ...................................................................... 108 5.1.2 Raja Ampat coral reef ecosystem ............................................................. 109 5.1.3 Combining ecosystem model and Bayesian belief network ..................... 110 5.2 Methods ............................................................................................................ 111 5.2.1 Ecopath with Ecosim ................................................................................ 111 5.2.2 Model structure of the influence diagram ................................................. 111 5.2.3 Discounting ............................................................................................... 122 5.3 Results .............................................................................................................. 123 5.3.1 Linear utility functions .............................................................................. 123 5.3.2 Discounting ............................................................................................... 127 5.3.3 Non-linear utility functions ....................................................................... 129 5.4 Discussion ........................................................................................................ 131 5.4.1 Policy choice ............................................................................................. 131 5.4.2 Discounting ............................................................................................... 132 5.4.3 Utility functions ........................................................................................ 133 vi 6 5.4.4 Tourism revenue ....................................................................................... 134 5.4.5 Conservation utility................................................................................... 135 5.4.6 Other anthropogenic impacts .................................................................... 135 5.5 Conclusion........................................................................................................ 135 5.6 References ........................................................................................................ 137 The Influence of Life History Parameters in Fish Population Restoration ............. 144 6.1 Introduction ...................................................................................................... 144 6.2 Methods ............................................................................................................ 146 6.2.1 Biomass per recruit (B/R) ......................................................................... 146 6.2.2 Recruitment ............................................................................................... 147 6.3 6.3.1 Biomass per recruit ................................................................................... 154 6.3.2 Recruitment ............................................................................................... 157 6.4 7 Results .............................................................................................................. 154 Discussion ........................................................................................................ 161 6.4.1 Selectivity ................................................................................................. 161 6.4.2 Biomass per recruit ................................................................................... 162 6.4.3 Recruitment ............................................................................................... 164 6.5 Conclusion........................................................................................................ 169 6.6 References ........................................................................................................ 171 Conclusion .............................................................................................................. 176 7.1 References ........................................................................................................ 186 Appendix A Ecosystem Based Management: the Influence of a Project in Raja Ampat, Papua, Indonesia ............................................................................................................. 191 Appendix B Ecopath Parametrization of Raja Ampat Model ................................... 201 Appendix C Estimation of IUU Fishing in Raja Ampat ............................................ 215 vii Appendix D Dispersal Rates in Raja Ampat Ecospace Model .................................. 241 Appendix E Creation of Sub-Area Models ............................................................... 248 Appendix F Probabilities Tables of the Bayesian Influence Model.......................... 256 Appendix G Previously Published Work Related to the Thesis ............................... 263 viii List of Tables Table 2.1 Absolute estimates for IUU catch ranges. ........................................................ 35 Table 2.2 IUU catch in thousand tonnes in 2006 and the error on the estimates.............. 38 Table 2.3 Total revenue from IUU fishing for 2003-2006 and error on the estimates ..... 40 Table 4.1 VBGF parameters for modern snapper populations. ........................................ 85 Table 5.1 Utility from fisheries revenue, tourism revenue, and conservation benefits when all the three sources of utility are modeled with linear utility functions ........................ 127 Table 5.2 Utility from fisheries revenue, tourism revenue, and conservation benefits. . 130 Appendix Table A-1 EBM Scores .................................................................................. 193 Appendix Table B-1 Basic parameters of the 2005 Raja Ampat Ecopath model ........... 205 Appendix Table C-1 Influence table ............................................................................... 215 Appendix Table C-2 Predicted incentives for each year for each fishery ...................... 231 Appendix Table D-1 Dispersal rates in Ecospace model................................................ 242 Appendix Table E-1 Hard coral coverage reported for Raja Ampat .............................. 250 Appendix Table E-2 Area occupied by mangroves ........................................................ 251 Appendix Table E-3 Perimeter of coastline .................................................................... 252 Appendix Table E-4 Area < 200 m depth ....................................................................... 252 Appendix Table F-1 Probability table for node restored ecosystem state ...................... 256 Appendix Table F-2 Probability table for node fisheries catch ...................................... 257 Appendix Table F-3 Probability table for node average price ........................................ 258 Appendix Table F-4 Probability table for node fisheries revenue .................................. 259 Appendix Table F-5 Tourism projection ‗low‘ for restored ecosystem states................ 260 Appendix Table F-6 Ttourism projection ‗high‘ for restored ecosystem states ............. 260 Appendix Table F-7 Benefits for conservation modeled as WTP .................................. 261 Appendix Table F-8 Benefits of conservation modeled as ES ....................................... 261 Appendix Table F-9 Probability tables for tourism revenue and conservation benefit combined ......................................................................................................................... 262 ix List of Figures Figure 2.1 Influence trend. ................................................................................................ 33 Figure 2.2 Quantifying incentive for unreported reef fish fishery. ................................... 34 Figure 2.3 Example distribution of the error assumption. ................................................ 37 Figure 2.4 Reported and unreported catches in Raja Ampat. ........................................... 39 Figure 2.5 Total revenue from IUU fishing in Raja Ampat (2003-2006). ........................ 40 Figure 3.1 Map of Raja Ampat ......................................................................................... 58 Figure 3.2 Example of the closure patterns in Kofiau Ecospace model. .......................... 63 Figure 3.3 Relative biomass changes inside MPAs. ......................................................... 65 Figure 3.4 Influence of dispersal rate on biomass density change. .................................. 66 Figure 3.5 Relative catch changes inside MPA ................................................................ 68 Figure 3.6 Figure 6 Biomass change in different MPA configurations. ........................... 69 Figure 4.1 Map of North Island of New Zealand.............................................................. 83 Figure 4.2 Comparison of length frequency data for ancient and modern snapper populations. ....................................................................................................................... 87 Figure 4.3 Candidate growth curves for ancient population. ............................................ 89 Figure 4.4 Estimation of total mortality (Z)...................................................................... 91 Figure 4.5 Vulnerability at age to fishing ......................................................................... 94 Figure 4.6 Proportions of large fish (>750 mm and 800 mm) estimated using modern published and candidate ancient growth curves................................................................ 98 Figure 4.7 Ancient snapper population biomass. .............................................................. 99 Figure 5.1 Structure of influence diagram. ..................................................................... 112 Figure 5.2 Ecosystem states and restoration goals.......................................................... 114 Figure 5.3 General shape of utility functions used in the analysis ................................. 119 Figure 5.4 Decay of future benefits at different discount rates used in the analysis. ..... 123 Figure 5.5 Utility of fisheries revenue. ........................................................................... 124 Figure 5.6 Utility of tourism revenue. ............................................................................ 125 Figure 5.7 Utility from conservation benefits. ................................................................ 125 Figure 5.8 Comparison of discounted benefits from fisheries and tourism. ................... 128 Figure 6.1 Beverton-Holt stock recruitment curves at different values of steepness. .... 153 x Figure 6.2 Plot of log biomass per recruit against survival. ........................................... 155 Figure 6.3 Slope and intercept of log biomass per recruit against growth coefficient k. 155 Figure 6.4 Plot of log biomass per recruit against survival at 2 levels of maximum age. ......................................................................................................................................... 156 Figure 6.5 Change in equilibrium mean recruitment with change in survival for 2 species. ......................................................................................................................................... 157 Figure 6.6 Mean recruitment curves for ~1800 species plotted against total mortality Z. ......................................................................................................................................... 160 Appendix Figure A-1 Scores for EBM principles .......................................................... 195 Appendix Figure A-2 Scores for EBM indicators .......................................................... 195 Appendix Figure A-3 Scores for EBM implementation ................................................. 196 Appendix Figure B-1 Trophic flows in the Raja Ampat marine ecosystem ................... 210 Appendix Figure B-2 Feeding interactions identified by stomach sampling that were not predicted by diet allocation algorithm. ........................................................................... 213 xi Acknowledgements I thank my supervisor, Tony Pitcher for his whole-hearted support and guidance without which this thesis would have been impossible. It is around his ideas and visions for fisheries management that this thesis is developed. I am especially grateful to Cameron Ainsworth for his guidance and mentorship during the first two years of my PhD. He taught me many of the skills which I have used in my thesis. I thank my supervisory committee member Rashid Sumaila for guidance in all the studies with an economic component, and for saying ‗keep pushing‘ when I needed it very much. My skills with using the R program and making fisheries models, I learnt from Steve Martell and I thank him for the same. I thank him for helping me improve the work I present in Chapters 4 and 6. I acknowledge our partners in Indonesia, especially Peter Mous (COREMAP II), Mark Erdmann and Chris Rotinsulu (Conservation International), Mohammed Barmawi and Jos Pet (The Nature Conservancy Coral Triangle Centre). I also thank Yohannes Goram, Andreas Muljadi, Rein Paat, Obed Lense, Muhammad Syakir for their valuable input and discussion on the influences in the Raja Ampat fishery. I acknowledge Becky Rahawarin (Kepala Dinas Perikanan dan Kelautan, Raja Ampat) for helpful discussions. I thank cooperation and continued interest in the work from fisheries archaeologist Foss Leach for interest and support in exploring archaeological data for fisheries reconstruction. I thank Alison MacDiarmid for continuous encouragement and logistic support for the work in Chapter 4 on snapper population reconstruction. I thank Murdoch McAllister for his encouragement and guidance with the work in Chapter 5 that uses Bayesian influence diagrams. I thank Daniel Pauly for short pertinent discussions on some questions I brought to him regarding fish life history which is explored in Chapter 6. I thank Robyn Forrest for her suggestions to explore ‗steepness‘ of recruitment curves for Chapter 6 and for being a mentor and friend during the early years xii of my PhD. I thank Megan Bailey for her enthusiastic cooperation during our work together on the ecosystem based management project for Raja Ampat. I thank all the members of the PERF research group for friendships, suggestions, and questions and over the last 5 years – Nigel Haggan, Telmo Morato, William Cheung, Hector Lozano, Eny Buchary, Pramod Ganapathiraju, Carie Hoover, Mimi Lam, Lingbo Li, Lydia Teh. I also thank my friends at the Fisheries Centre especially Chiara Piroddi, Colette Wabnitz, Erin Rechinsky, Jennifer Jacquet, Meaghan Darcy, Rachel Louton, Louise Teh, Jonathan Anticamara, Sarah Foster and Shannon Obradovich. I also thank Laura Tremblay-Boyer for help with math and programming. Chapters 2 and 3 were funded by a grant from the David and Lucille Packard Foundation to TNC and University Graduate Fellowship. Chapter 4 was funded through Peter Wall Institute of Advanced Studies through the Scholar in Residence program awarded to Tony Pitcher. Chapter 5 was funded through University Graduate Fellowship and John Corry Fellowship. I thank Rajeev Kumar my husband for proofreading my thesis, for his incredibly patient support, and for his generosity with his time. He ensured my sanity during the writing of the thesis. xiii Dedication xiv Co-Authorship Statement Chapter 1 and Chapter 7 are the introductory and concluding chapters of the thesis. All the other chapters are written as publishable manuscripts. Chapter 2 is published, Chapter 3 has been submitted and Chapters 4 to 6 are yet to be submitted to for publication. I am the lead author on all the manuscripts and assume responsibility for the analyses and the results presented. Chapter 2 is co-authored with Cameron Ainsworth, Tony Pitcher, Yohannes Goram, and Rashid Sumaila. The methodology was based on previous work by Tony Pitcher and Cameron Ainsworth. Yohannes Goram provided several key insights into local aspects of the fisheries in Raja Ampat. Rashid Sumaila provided guidance with the economic aspects of the analyses. Chapter 3 is co-authored with Cameron Ainsworth and Tony Pitcher. The Ecospace models are built upon Ecopath and Ecosim models for Raja Ampat. Cameron Ainsworth and I built the Ecopath and Ecosim models. Tony Pitcher provided guidance in all aspects of the research. Chapter 4 is co-authored with Tony Pitcher, Foss Leach and Alison MacDiarmid. The original idea for this work was developed by Tony Pitcher and Foss Leach. Foss Leach also provided the archaeological data used in the analyses. Alison MacDiarmid provided information and guidance on the modern aspects of the snapper fisheries in New Zealand. Chapter 5 is co-authored with Tony Pitcher, Murdoch McAllister, and Rashid Sumaila. Tony Pitcher provided overall guidance in setting up the questions for the analysis. Murdoch McAllister provided guidance on the use of bayesian influence models. Rashid Sumaila provided guidance with the economic aspects of the chapter. Chapter 6 is co-authored with Tony Pitcher who provided guidance and edits at different stages of the work. xv 1 Introduction This thesis asks the following questions with regard to marine ecosystem restoration. (1) What are the management challenges in a tropical coral reef ecosystem (Raja Ampat, Indonesia)? (2) What is an ideal minimum size for a marine protected area (MPA) from an ecological perspective? (3) What is the carrying capacity of a species in a system? (4) How can multiple uses from the ecosystem be considered together? And lastly, (5) how do life-history parameters of fish influence the response of a population to restoration? This introductory chapter begins with a brief description of the history of fisheries management; mainly the events and changes that have led to the current focus on restoration. The chapter also narrates the history of marine resource use and the current management status in Raja Ampat. Raja Ampat is a regency (the administrative hierarchy of a regency is one level below the province and roughly corresponds to a district) located adjacent to the northwest tip of the province Papua in Eastern Indonesia. Finally, this chapter discusses the key questions asked in each chapter of the thesis. 1.1 Rationale: brief history of fisheries management At the great international Birkbeck‘s fishery exhibition held in 1883 to celebrate success of British fisheries (Nature News 1883), it was debated whether fisheries were exhaustible or not, and whether there was need of fisheries management (Smith 1994). Though with many qualifications—referring only to pelagic fish and to the then present mode of fishing —Thomas Huxley maintained that fisheries were inexhaustible and nothing humans did could affect the numbers of fish in the oceans (Smith 1994). Huxley‘s arguments were countered by Ray Lankester (Smith 1994), who mainly raised concerns about recruitment overfishing. Complaints against the destructive nature of trawlers were older, but inquiry commissions and researchers repeatedly exonerated trawl fisheries mainly based on inconclusive evidence; all forms of fisheries were allowed 1 ‗unbridled expansion‘ (Roberts 2007). When evidence1 was made available in the form of declining catch per unit effort (CPUE), it was not heeded enough (Smith 1994). Shifting baselines (Pauly et al. 1998) ensured that even in the 1950s, the perception of inexhaustibility continued2. However, in the period from the late-1800s to the mid-1990s, the science of the study of fish populations and fisheries management had grown, and more evidence was collected on the potential of fisheries to impact fish populations.3 It was recognized that fisheries management was advantageous; this itself was a significant step forward from the previous century when fisheries had been allowed to grow unhindered (Roberts 2007). Some of the important scientific works that continue to be extensively used today include the catch equation given by Baranov (1918) and the growth model given by von Bertalanffy (1938). Population growth was described as a sigmoid curve (Sigmoid curve theory by Graham, 1935), and further progress on the same idea led to the development of the ‗surplus production model‘ (Schaefer 1954). The Schaefer surplus production model probably, on account of its ease of application and focus on maximizing yield, became very popular, and was applied to almost every fishery. The Schaefer surplus production model was probably the first scientific work that theoretically showed that excessive effort could lead to population declines. Major changes in fisheries management happened in the period 1950 to 2000. Large, long-distance fleets spread to distant coastlines; perceptions of fisheries declines gained strength, and several nations proceeded to secure their coastlines. To quell the increasing conflict over the seas, The United Nations Convention on the Law of the Sea (UNCLOS) established the ‗Exclusive Economic Zone‘, and most countries signed the UNCLOS in 1982. Each coastal state had sovereign rights over the resources in the adjacent continental shelves and was responsible for managing and conserving the same resources. All states subsidized fisheries and fisheries catch increased all over the world. The 1 The impoverishment of the Sea‘ by Walter Garstang published in 1900, Walter Garstang evidently stated that fisheries were exhaustible and were in the process of being exhausted, cited from Smith (1994). 2 The inexhaustible sea‘ by H. Daniel and F. Minot, published in 1954, cited from Roberts (2007). 3 Russell‘s 1942 lecture on ‗Overfishing‘, and 2 post-war increase in fish abundance in North Sea, cited from Beverton and Holt (1957). 2 maximum sustainable yield (MSY), the maximum of the surplus production from a stock that can be sustainably harvested each year, became the ―key paradigm‖ and ―played a central role‖ in management (Punt and Smith 2001). The MSY was estimated most commonly using Schaefer‘s surplus production model given in the 1950s (Schaefer 1954). There were several problems with the MSY approach including the assumption of equilibrium and the assumption of CPUE being directly proportional to abundance (Larkin 1977; Sissenwine 1978; Punt and Smith 2001). When surplus production models were applied to growing fisheries, the debt associated with exploiting standing stocks of populations was overlooked, and by the time the problem was recognized, several fisheries had exceeded their MSY levels, and the fishing industry had become overcapitalized. By late 1980s it was recognized that fisheries could not ―sustain uncontrolled exploitation and development‖ (FAO 1995). Consequent to overcapitalization, the goal of fisheries management was to control fishing capacity. Stock assessment models more complex than surplus production models were developed. The virtual population models which included both catch and age information had originated earlier (Derzhavin 1922; cited in Sparre and Venema 1998), but they became popular from 1950 to 2000 (Megrey 1989; cited in Sparre and Venema 1998). Dynamic pool models were introduced in the 1950s (Beverton and Holt 1957). Complex fisheries stock assessments methods continued to evolve to provide accurate management advice on quotas, harvest control rules, fixed escapement rules, and reference points (Pauly and Morgan 1987; Hilborn and Waiters 1992; Hannesson 1993; Walters and Pearse 1996; McAllister and Kirkwood 1998; Cooke 1999; Walters and Martell 2004). However, the extraction levels suggested by science were often negotiated upwards at political negotiations (Daw and Gray 2005). Restrictive regulations met with resistance from the fishers—the fishers adopted the restrictions only when their opportunity cost for fishing elsewhere or altogether leaving fishing were higher (Clark 2006). In addition, the lack of compliance led to underreporting and issues related to illegal, unreported and unregulated (IUU) fishing (Pitcher et al. 2002; Sumaila et al. 2006) became serious. Inaccurate catch data can lead to inaccurate management recommendations (Patterson et al. 2001). Several schemes were introduced to control fishing capacity, but most of the 3 schemes were made toothless by the clever strategies of the fishers. Vessel buy back schemes, especially when they were anticipated, were ―economically equivalent to direct vessel subsidies‖ because the fishers invested in capacity and used these schemes to get rid of inefficient vessels (Clark 2006). Measures to limit entry led fishers to invest in increasing fishing power by increasing engine, gear or fishing hold capacity (capital stuffing) (Clark 2006). The difficulty in reducing fishing capacity was further exacerbated by non-malleable (fishing vessels that could not be easily converted to other uses) or only partially malleable fishing fleets (Clark 2006). The precautionary principle became popular in fisheries science in the early 1990s to reduce the chance of collapse of exploited species and to limit impacts on non-target species and habitats (Garcia 1994; Costanza et al. 1999). The FAO Code of Conduct for Responsible Fisheries (FAO-CCRF) adopted in 1995 provided principles for conservation, management and development of fisheries resources: the conservation guidelines promoted precautionary approach, advocated limit reference points, protection of critical habitats, and recovery of depleted stocks (FAO 1995). An extensive study (Pitcher et al. 2009) a decade after the FAO-CCRF was adopted found that several developed and developing countries performed poorly with respect to the adopted standards. The last two decades (1990-2010) documented the tragedies of overfishing and raised concern regarding the health of the oceans. Unchecked overcapacity led several fisheries to collapse; one of the most notable was the unimaginable setback from the collapse of the North Atlantic cod. Fish populations declined worldwide (Myers et al. 1996; Rose and Kulka 1999; Morris et al. 2000; Dulvy et al. 2003; Hutchings and Reynolds 2004). Declining fish populations pushed several marine ecosystems towards collapse (Hughes 1994; Pauly et al. 1998; Jackson et al. 2001; Pandolfi et al. 2003). Dulvy et al. (2003) documented 133 local, regional, and global extinctions. 1.2 Context This section includes a brief description of the study area, the history of marine resource use, and the current status of management in Raja Ampat. 4 1.2.1 Brief description of study area - Raja Ampat Islands Raja Ampat is a Regency located within the northwest tip of the province Papua in eastern Indonesia. The region is an archipelago that extends over 45,000 km2, it includes 4 large islands (Waigeo, Batanta, Salawati and Misool), and around 600 small islands. The archipelago is located in the ‗Coral Triangle‘ (Donnelly et al. 2003). The area encompasses a variety of marine habitats including some of the most biodiverse coral reef areas on Earth (McKenna et al. 2002a; Donnelly et al. 2003). It is estimated that Raja Ampat possesses over 75 percent of the world‘s known coral species (Halim and Mous 2006). More than 1000 fish species, manta rays, sharks, and short finned pilot whales and turtle rookeries, are the other highlights of marine life in the region. Several authors (Allen 2002; Erdmann and Pet 2002) have referred to the exceptional habitat diversity and consequent rich biodiversity of the region. The most abundant reef fish families in the region are gobies (Gobiidae), damselfishes (Pomacentridae), wrasses (Labridae), cardinalfishes (Apogonidae), groupers (Serranidae), butterflyfishes (Chaetodontidae), surgeonfishes (Acanthuridae), blennies (Blenniidae), parrotfishes (Scaridae), and snappers (Lutjanidae). These 10 families represent 61% of reef fish species in Raja Ampat (Allen 2002). A survey across 45 reef sites revealed that most of the reef sites were in ‗excellent‘ to ‗good‘ condition (measured based on reef condition index), but few sites were observed to be in poor condition (McKenna et al. 2002b). Stress and damage was observed on 85% of the surveyed sites; the predominant stressors were fishing pressure (including destructive fishing methods), siltation, and eutrophication/pollution. From 1960 to 1993, the human population has increased in the region at an average rate of 3% per year (2.7% in 1960 to 1980 (McNicoll 1982); 3.41% in 1980 to 1990 (Surbakti et al. 2000)). Small-scale fisheries operations on the reefs and in the inshore areas provide livelihoods for around 24,000 fishers (Dohar and Anggraeni 2007). A total of 196 species, representing 59 genera and 19 families, are classified as target species for reef fisheries in Raja Ampat (Tanda 2002). Reef fish constitutes about 40% of the catch by the local fishers; the remainder of the catch is contributed by Spanish mackerel, sea 5 cucumber, snails, and lobsters in almost equal proportions (Muljadi 2004). The fishing gear types used in the region include spear fishing, reef gleaning, shore gillnets, driftnets, permanent and portable traps, spear diving (for fish and invertebrates), diving specifically for live fish (with or without the aid of cyanide), blast fishing using dynamite, trolling, purse seining, pole and line, set lines, lift nets, and shrimp trawls. The shrimp trawl fishery is located in the Arafura Sea, southeast of Raja Ampat. A foreign fleet, consisting mainly of powered Philippino tuna vessels, also operates in deeper areas in the north of Raja Ampat (Muljadi, A4. pers. comm.). 1.2.2 Historical and political background of marine resource use in Raja Ampat It is important to understand resource use history because the current perceptions towards management are based on the events in the history5. Importance of fisheries resources to the people of Raja Ampat increased after 10th century AD. The people in Raja Ampat communicated with people from Biak, Seram, Central Mollucas, and south of Papua to Fak Fak and began barter of marine snails (Trochus spp.), turtle etc. By 13th century they learnt the technology to make canoes. Marine resource use increased from the 14th to 16th centuries with the formation of a trading triangle with the Sultanate of Ternate and Tidore in the north. There were increased interactions of regional people with the seafarers and traders from Biak (Ploeg 2002), who came to anchor and fish for a couple of months annually in Raja Ampat. Over time, migrants from Biak and the Mollucas began to live in Raja Ampat islands. During the 17th-19th century, fish catch from Raja Ampat was sent as tax to the King of Mollucas who had become the King over Raja Ampat after defeating the local King. 4 Andreas Muljadi TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. 5 This account is written based on an unpublished account that belongs to the Council of Traditional Ethnic Groups in Raja Ampat: Dewan Adat Suku Maya Kepulauan Raja Ampat (The Council of Traditional Ethnic Groups in Raja Ampat) 2006. Sejarah pemanfaatan sumberdaya alam di kepulauan Raja Ampat (Perspektif Adat) The history of the utilization of nature resources in the island of Ring Ampat (Traditional perspective) 6 Towards the end of 19th century, the Dutch established control over the Mollucas and Papua (then referred to as Netherlands East Indies jointly) (Ploeg 2002). After World War II, Papua became a separate administrative unit under Dutch command. Papuans were trained to hold lower and middle level administrative positions. When Indonesia secured independence in 1949, Papua was not part of the sovereign territory but remained under Dutch control. Negotiations were continued between the Dutch and the Indonesian governments regarding the fate of Papua. Finally in 1962, the Dutch ceded control over Papua6 to Indonesia, and in May 1963, Indonesia took over the administration of the region. From then on, Raja Ampat and other regions in Papua witnessed immigration (locally referred to as ―Indonesianization‖) from other provinces of Indonesia including Java, Sulawesi, and Sumatra. The period under Indonesian government rule is regarded as a time of discrimination in which Papuans had fewer rights than Indonesians; in fact, ―the rivalries and antagonism between Papuans and Indonesians were even more apparent after Indonesia took control‖ (Chauvel 2005). From the perspective of marine resource use in Raja Ampat, the migrants introduced different kinds of gears and crafts based on the skills that belonged to the regions where they had come from. The immigrant fishers began to catch fish and sell them to Java and Sumatra. The immigrants did not recognize the ‗adat‘ values (traditional resource management principles) since all resources were now supposed to be owned by the state. There was a gradual transformation from a subsistence-based lifestyle to a cash-based economy. At the national level, interest in management of the fisheries sector grew at a gradual pace in Indonesia. Repelita IV (Rencana Pembangunan Lima Tahun - Five Year Development Plan) in 1984 and Repelita VI in 1994 placed emphasis on integrated coastal zone management including ―fish production and environmental protection of marine areas‖ (Patlis et al. 2001). An independent Dinas Kelautan dan Perikanan (DKP – the Ministry of Marine Affairs and Fisheries) was established in 2000 (Patlis et al. 2001). 6 From 1969 to 1973 the region was referred to as ‗West Irian‘ and ‗Irian Barat‘, after which the region was renamed as ‗Irian Jaya‘. Later in 2002, the name ‗Papua‘ was adopted. 7 Political reformation in Indonesia from the centralized Suharto regime (New Order 1965) to a decentralized government (Act no. 22/1999 on regional autonomy and Act no. 25/1999 on financial relations) gave more powers to the provincial and regency governments. The provinces were allowed authority up to 12 nautical miles from the coastal shoreline including ―supervision of fishery resources, licensing of permits for catching, and cultivating fish‖ while regencies were allowed authority within 4 nautical miles from the shoreline. These acts specially mentioned that traditional fishing rights would not be restricted by the ―regional territorial sea delimitation‖. Except for few areas of governance, the regencies had the authority for all decision making within their jurisdiction. Regencies, because they had the political authority and were in close proximity with the resource users, had the ability to establish management programs adapted to local interests (Patlis et al. 2001). 1.2.3 Current management in Raja Ampat A decree by the Bupati (Regent) in 2003 declared Raja Ampat a Maritime Regency ‗Kabupaten Bahari‘ (Conservation International 2008). The goals of the Raja Ampat Regency are to improve the welfare and prosperity of the community by promoting fisheries, conservation, and tourism while respecting customary rights (Raja Ampat Regency 2007). The regency established a new network of marine reserves in 2006 covering more than 650,000 hectares of sea area and 44% of reef area in Raja Ampat. The DKP pledged that 30% of the marine area of Raja Ampat would be declared as protected zones, exceeding the national goal of 20%, and that no-take areas would be established within the protected zones (Rahawarin, B7. pers. comm.). In February 2002, the Papuan Traditional Council (Dewan Adat Papua) held the Papuan congress. The goal of the council was to integrate the indigenous and immigrant population in Raja Ampat within the traditional adat and to revive the traditional marine tenure in the region in collaboration with the fisheries management department in Raja Ampat. Studies of fisher perceptions in Raja Ampat showed that the fishers believed that fish catch had declined over the past 10 to 20 years (Muljadi 2004; Ainsworth et al. 7 Becky Rahwarin DKP, Raja Ampat. Jl. A. Yani, Kuda laut, Sorong, Papua. 8 2008). The main threats to management in the region, as recognized by the regency and the local population, were blast fishing, cyanide fishing, fishing by migrant fishers, and overfishing (Muljadi 2004; Raja Ampat Regency 2007) In 2005, concerned with the issues of fisheries management and with the intention to develop environmentally sound ecosystem based policies, the regency government participated in a collaborative project—the Birds Head Seascape Ecosystem Based Management (BHS EBM) project—funded by the David and Lucille Packard Foundation. The BHS EBM project involved three environmental NGO partners (Conservation International, The Nature Conservancy‘s Southeast Asia Center for Marine Protected Areas, and WWF-Indonesia) in a science-based initiative in partnership with local stakeholders to explore ecosystem processes relevant to management. This author conducted an evaluation of the expected progress from the successful implementation of the project. The evaluation was based on the framework of Ward et al. (2002) framework on Ecosystem Based Management (EBM); the framework evaluates EBM using five overall principles, six criteria for success, and twelve implementation steps. The project was able to increase awareness on the threats to coral reef resources (mainly destructive fishing methods in the region). The project was also able to fill several gaps of information by conducting surveys on reef health and fishing effort. During the project conducted demographic surveys were conducted to evaluate fisheries and other economic sectors. An inventory on habitats and eco-regions was created and ecosystem models were built for analysis of policies for fisheries management. For details of the evaluation of EBM in Raja Ampat please refer to Appendix A at the end of the thesis. It is expected that successful implementation of the project will improve the management status of the region considerably. 9 1.3 Thesis focus: restoration in fisheries with focus on coral reef ecosystems 1.3.1 Restoration within an EBM framework With the increase in accounts of marine declines, the emphasis on restoration has increased (Pitcher 2001; Fox et al. 2003; Russ and Alcala 2003; Lotze et al. 2006). For example, on World Oceans Day in 2010, legislators from predominant fishing nations agreed on a ‗Global Marine Recovery Strategy‘ to restore the declined fish populations (GLOBE 2010). Moreover, recent assessment of the status of exploited fish stocks emphasized the need for large scale effort at rebuilding marine ecosystems, but also stated that recovery and rebuilding were poorly understood (Worm et al. 2009). Studies of historical and archaeological evidence (Jackson et al. 2001; MacKenzie et al. 2002; Lotze and Milewski 2004; Roberts 2007; Rose 2007) showed the high abundances of species in ancient ecosystems. It was suggested that the historical levels of population abundance be used as goals for rebuilding the current ecosystems (Pitcher and Pauly 1998; Pitcher 2001). Decreasing fishing capacity and establishing MPAs are two key tools to ensure rebuilding (Pauly et al. 2002). Though MPAs have been advocated atleast as early as 1997 as valuable insurance against environmental and management uncertainty (Roberts 1997), MPAs have also faced considerable scepticism about their value in restoring species abundances (Willis et al. 2003). More recent work has shown that different species respond differently to protection (Claudet et al. 2006; McClanahan et al. 2007; Molloy et al. 2009). Placement and design of MPAs has also been widely researched both from theoretical and practical perspectives (Walters et al. 1998; Ball and Possingham 2000; Halpern 2003; Lubchenco et al. 2003; Shanks et al. 2003). In addition to the focus on restoration and rebuilding, increased need was observed to understand the inter-relationships of species and their consequences in fisheries management decisions (Link 2002a; Christensen et al. 2007). The concept of EBM to incorporate issues beyond single species questions, began becoming popular in 1990s (Szaro et al. 1998; Link 2002b). Incorporation of ecosystem approaches in fisheries management was discussed at the FAO conference on responsible fisheries (FAO 2002) 10 conference and guidelines were published in 2003 (Garcia et al. 2003). Ward et al. (2002) proposed a framework for EBM based on three sets of attributes: overall principles (5 attributes; Table 2, page 19 in Ward et al. 2002); criteria for success (6 attributes; Table 3, pages 19-20 in Ward et al. (2002); and implementation steps (12 attributes; Table 6, pages 50-51 in Ward et al. 2002). The attributes included a broad range of concepts from data gathering, recognising ecosystem values to involving stakeholders in management. Legislative requirements in several countries demanded the inclusion of principles of EBM (Hall and Mainprize 2004); numerous international conventions also required this type of holistic view (Garcia et al. 2003). In spite of its popularity, EBM continued to remain an ―elusive concept‖ that was interpreted differently by different users (Hilborn et al. 2004). In 2009 (McLeod and Leslie 2009), more than 200 scientists and policy experts agreed on a common goal of EBM as ―conservation of the long-term potential of ecosystems to deliver of a broad suite of ecosystem services‖ and also agreed on a definition for EBM—―key aspects of the definition include: (1) considering the entire ecosystem, including humans; (2) taking an integrated view across species, sectors, activities, and concerns; (3) evaluating cumulative impacts across sectors; (4) emphasizing the protection of ecosystem structure, functioning, and key processes; (5) accounting for the interconnectedness within and among systems; and (6) recognizing the interdependence among ecological, social, economic, and institutional perspectives.‖ EBM in Great Barrier Reef Marine Park is referred to as ―gold standard for EBM‖, and the success has been attributed to ―equal attention to human and natural‖ aspects (Ruckelshaus et al. 2008). 1.3.2 Coral reefs Coral reefs are magnificent marine ecosystems; their incredible biodiversity supports numerous types of livelihoods. Coral reefs are characterized by three main features: (1) high species diversity (Sale 1977; Connell 1978), (2) complexity of relationships (Sale and Douglas 1984; Hixon and Beets 1993), and (3) high rates of production (Lewis 1977). The species richness and composition of ―functional groups‖, species occupying the same niche or delivering the same function within an ecosystem, on reef ecosystems play an important role in the ability of the ecosystems to respond to fishing and other 11 stressors (Bellwood et al. 2004). Changes in both target and non-target reef fish communities (Jennings et al. 1995; Jennings and Polunin 1996; Jennings and Polunin 1997; Sala et al. 1998), benthic and algal communities (Sala et al. 1998) have been attributed to fishing. The changes ultimately alter the competitive balance and associated trophic structure among reef communities (Roberts 1995; McClanahan 1997). Local abundances of coral-reef fish are also determined by the relative magnitudes of larvae recruitment, colonization by juveniles and adults, predation and competition for refuges—each of them varies through time and space (Swearer et al. 1999). A global review of the status of coral reefs found that several coral reef ecosystems had declined; the review suggested that management for status quo was a ―weak‖ goal; rather efforts should be made to restore the reefs (Pandolfi et al. 2003). Similar to the changes observed with fishing, recovery is also dependent on the trophic composition of reef ecosystems (Mumby et al. 2006; Hughes et al. 2007). Building an ecosystem model of the coral reef ecosystem can offer insights into options for management and recovery of the ecosystem. 1.3.3 Ecopath with Ecosim This thesis uses ecosystem models to represent the species interactions on coral reefs. Ecosystem models are able to integrate information from different components of the ecosystem. Ecopath with Ecosim (EwE) incorporates biological information on species with fisheries catch information to explore the effects of species and fisheries interactions. The EwE models help to understand ecosystem behaviour and to assist analysis of trade-offs in marine policy (Christensen and Walters 2004a). Since its origins, (Polovina 1984) in the span of 25 years the modeling tool EwE has advanced considerably (Christensen 1992; Walters et al. 1997; Walters et al. 1998; Pauly et al. 2000; Walters et al. 2000). The various capacities of the EwE include modeling trophic linkages, life history stanzas of species, simulation of fisheries impacts, optimal policy searches and so on. Ecosystem modeling using EwE has become very popular (Christensen and Walters 2005); a total of more than 100 EwE models have been built with at least one EwE model for almost all (excluding some polar regions) large marine ecosystems (LMEs). 12 EwE and its spatial component Ecospace are used to represent the food web of Raja Ampat and simulate trophic interactions of interest to fisheries and conservation. Ecopath provides a ―static picture of the ecosystem trophic structure‖ (Walters et al. 1997). The ecosystem components are summarized into functional groups (species aggregated by trophic similarity). Ecopath describes the flux of matter and energy in and out of each group and models human influence through fishery removals. Ecosim allows modeling of species composition changes over time (Walters et al. 1997) and exploration of past and future effects of fishing (Christensen and Walters 2004b). EwE models have been used in fisheries management to a limited extent (Christensen and Walters 2005). Reviews and criticisms of the EwE approach (Fulton et al. 2003; Christensen and Walters 2004b; Plagányi and Butterworth 2004; Plagányi 2007) highlight the strengths and weaknesses of the modelling approach. Please refer to Appendix B for details on parameterization of the EwE models for Raja Ampat. Restoration scenarios are explored for the coral reef ecosystems in Raja Ampat are explored using the models. The ideas for fisheries restoration in the thesis are developed within the framework of EBM of coral reefs. In brief my thesis explores the history of events in the region to understand management challenges, explores multiple utilities of stakeholders from the perspective of marine ecosystem restoration, explores ecosystem response in marine protected areas to protection using ecosystem models, explores carrying capacity of a species in a system, and explores the influence of life history in the response of a species to protection. 1.3.4 Thesis outline Catch data missing from records is an ecological and a management problem. It is an ecological problem because when missing catch is not accounted for in stock assessment or in ecosystem models then over-optimistic levels of resource status may be estimated (Pauly et al. 2002; Pitcher et al. 2002). The optimistic estimates can lead to limited management controls or increased investment into fisheries development which can further deplete the resource. From a management perspective, the most basic functions of a management agency are to record or estimate the amount of catch and the number of 13 fishing operations in a region, so illegal, unreported and unregulated (IUU) catch is the first indication of flaws in management. Therefore, in Chapter 2, I explore the unreported catch in Raja Ampat to estimate the total extractions from the ecosystem and also to understand the management challenges in the region. In Chapter 2, the history of regulatory, technological, political, and market changes in the fishery from 1960 to present were analyzed using a method of ―semi-quantitative MonteCarlo integration of historical sources‖ originally developed by Pitcher and Watson (2000). The advantage of the technique was that all available data on under-reporting, no matter what regulatory regime was in place, could be combined to calculate the trends. The trends in the relative rate of misreporting of fisheries catch were estimated and converted to absolute values using anchor points. Anchor points were known rates of misreporting obtained either from the literature or from the surveys in the region or based on expert opinion. A Monte Carlo analysis was used to estimate the likely quantity of IUU catch with associated error ranges for six fisheries: reef fish, tuna, anchovy, shark, sea cucumber, and lobster. This method of estimating IUU catches has been used previously to estimate IUU for fisheries in the North Atlantic (Forrest et al. 2001), Iceland and Morocco (Pitcher et al. 2002), British Columbia, Canada (Ainsworth and Pitcher 2005), and Eritrea (Tesfamichael and Pitcher 2007). When this dissertation work was started, the coral reef ecosystem in Raja Ampat was a highly data-limited system; though, some information on fisheries landings was available. Therefore, adopting a method that could use multiple sources of data was essential. In addition, studying the history of the events led to greater understanding of challenges for fisheries management in Raja Ampat. The perspectives gathered in this study were useful in framing the questions addressed in the other chapters of the dissertation. After being declared a maritime regency, the Raja Ampat Regency government undertook the initiative to manage Raja Ampat on guidelines of EBM. Towards this goal, they set up a network of marine protected areas in 2006 (Conservation International 2008.). In Chapter 3, I analyzed ecological restoration from effort reduction and specific gear restrictions inside MPAs using the Ecospace model for Raja Ampat. Ecospace 14 integrates Ecopath and Ecosim across a two dimensional spatially explicit domain (Walters et al. 1998; Pauly et al. 2000). In Ecospace, functional groups which are linked by trophic relationships migrate between cells on a grid map of habitat (Walters et al. 1998; Pitcher and Buchary 2002). Ecospace models are useful tools to explore ecological changes in MPAs in response to change in fishing pressure. The research questions were identified through discussions with the Regency fisheries managers and scientific partners working in Eastern Indonesia. Ecospace has been used previously to explore changes in MPAs (Walters et al. 1998; Pitcher and Buchary 2002; Salomon et al. 2002; Jiang et al. 2008; Le Quesne and Codling 2009). Chapter 3 also explored no-take zoning options for Raja Ampat. I used high resolution sub-area Ecospace models for Dampier Strait, Misool and Kofiau (Islands in Raja Ampat) to compare outcomes between MPAs in several size combinations. This chapter also developed an ‗ideal minimum size‘ for an MPA—it is the minimum size of an MPA after which ecological benefits in terms of reef fish biomass density begin to asymptote. Suggestions on percentage of area to be closed as no-take in marine reserves range from 10 to 50% (Lauck et al. 1998; Dahlgren and Sobel 2000; Botsford 2001; Roberts et al. 2003; Parnell et al. 2006; Stewart et al. 2007). In a re-zoning effort, the no-take area in Great Barrier Reef Marine Park was increased six fold to 33%, and this no-take area includes at least 20% of each of the 70 bioregions (Olsson et al. 2008). Plans exist to designate 20% of North-western Hawaiian Islands Coral Reef Ecosystem Reserve as notake area (Hoegh-Guldberg 2006). The cited studies usually represent the percentage area to be closed or the percentage of the species population to be protected. Declaring percentages of habitats to be protected is useful at the level of international policy guidelines. However, at a local management level, a guideline on the area to be protected in square kilometers (like the one developed in the Chapter) would be more valuable. Among the 4 major islands of Raja Ampat, MPAs have been declared in Kofiau, Misool and Dampier islands. Kofiau is a smaller island in comparison and has a relatively homogenous population. Misool and Dampier are larger islands and have diverse populations. A proportion of the population in Misool are descendants of immigrants 15 from Sulawesi who still maintain trade and personal contact with their relatives in Sulawesi. There is lack of camaraderie between the indigenous Papuan population and immigrants who have settled from elsewhere. Chapter 3 thus tried to arrive at ecological solutions for restoration which offered flexibility in their application in the real world. In the real world optimum size of an MPA would depend on many factors which are extraneous to the ecological system. Chapter 4 used archaeological data to reconstruct the snapper population biomass on the west coast of New Zealand in ~1400 AD with the goal to understand ecosystem carrying capacity Studies based on historical and archaeological evidence have shown declines of several orders of magnitude in exploited species of marine mammals, and turtles, as well as Atlantic cod, also declines in coral cover; the past abundances have been suggested as targets for restoration (Jackson et al. 2001; Pitcher 2001; Rosenberg et al. 2005). Extensive fisheries archaeology work has been done in New Zealand; so my work was based on an archaeological fish population in New Zealand. The data consisted of the length frequency of archaeological New Zealand snapper (Chrysophrys auratus) population fished in early Maori times (~ 1400 AD). I decided to work on a single population since it was a more reliable approach than modelling (using scanty information) complex inter-species interactions in ancient ecosystems. The total mortality of the ancient population was estimated by fitting mixture distributions to the length frequency data. An equilibrium age structure model was applied to the growth and mortality information for calculating the ancient biomass. From the perspective of archaeological science, the methodology is highly useful since it provides a way to arrive at estimates of the ancient population based on data collected from archaeological middens. The estimates of the modern population were obtained from modern stock assessments and were compared with the results for the ancient population obtained in the chapter. Chapter 5 evaluated a suite of restoration scenarios for coral reef ecosystems in Raja Ampat for robustness to uncertainties of ecosystem status, tourism growth, interest in conservation, and utility functions of different stakeholders. Fisheries restoration always 16 put some restrictions on fishing activities. Sometimes fishers view the management body as an adversary (Hilborn 2007); at other times fishers themselves are interested in the protection of the environment (Crawford et al. 2004). Also, fishers are not the only stakeholders associated with using the ecological resources of the marine system. The groups of stakeholders I considered are the tourism industry and conservationists. In Chapter 5, I combined the results from ecosystem simulation model of Raja Ampat with projections of tourism and conservation benefits into an ‗Influence diagram‘ (special application of Bayesian network analysis) to evaluate alternate restoration scenarios for the Raja Ampat coral reef ecosystem. The restoration scenarios were modeled using the Ecosim model for Raja Ampat. Scenarios were evaluated based on different combinations of utility functions of the different stakeholders: fishers, tourism industry, and conservationists. The chapter also explored levels of expected revenue from tourism that could offset the losses to fishers under different restoration scenarios. Chapter 6 was devoted to understand how growth parameters of a species influenced restoration (i.e. the response to reduction in mortality). Two factors control the recovery of a population: biomass per recruit and increase in the number of recruits. The biomass per recruit (B/R) and was calculated using growth parameters, fishing mortality, and natural mortality parameters. The chapter analysed several combinations of mortality and growth parameters to determine if there was a pattern in the response of fish B/R to protection that depended on the growth parameters of the population. To determine the response in recruitment, the chapter analysed the range within which the equilibrium recruitment varied for any species at different levels of recruitment compensation. 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Science 325(5940):578. 28 2 Illegal, Unreported and Unregulated Fisheries Catch in Raja Ampat Regency, Eastern Indonesia8 2.1 Introduction 2.1.1 The IUU problem in Indonesia World-wide, illegal, unreported and unregulated (IUU) fishing prevents governments and resource managers from capturing the full economic rent from fisheries, and hampers the sustainable and ecologically responsible management of marine ecosystems (Pitcher et al. 2002; Agnew et al. 2008; Pitcher et al. 2009). Indonesia, with a reported catch of 4.7 million tonnes (average from 2002 to 2005) of fish and shellfish, is currently ranked as the world‘s sixth most important fishing nation (FAO 2008), but has a substantial problem with IUU catches in excess of those reported to government agencies and to FAO. As such, were the true estimates of catch to be considered, including large unreported extractions by both foreign and national vessels, and by both small scale fisheries and commercial fleets (Butcher 2002), Indonesia would probably rank higher in the list of top fishing nations (Pitcher et al. 2007). As highlighted in a synthesis of fisheries management issues in Indonesia, the high prevalence of IUU fishing in Indonesia can in part be explained by Indonesia‘s inefficient fisheries‘ data collection systems (Willoughby et al. 1999; Mous et al. 2005). For example, in western Bali, fishers land only about 45% of the catch at official landing sites, despite the close proximity of government landing sites (Buchary E.9 , pers. comm.). In the Arafura Sea, in Eastern Indonesia, Nurhakim et al. (2008) and Pitcher et 8 A version of this chapter has been published. Varkey, D.A., Ainsworth, C.H., Pitcher, T.J., Goram, J. and Sumaila, R. Illegal, unreported and unregulated fisheries catch in Raja Ampat Regency, Eastern Indonesia. Marine Policy 34: 228-236. 9 Eny Buchary, Ph.D. University of British Columbia. 29 al. (2007) estimated IUU catches in excess of one million tonnes per year; chiefly due to a lack of the financial and human capacity necessary to monitor and maintain accurate records. To compound this, the Fishery Act no. 9/1985 and the Fishery Act no.31/2004 do not require subsistence or traditional fishing vessels (i.e., fishing fleets ≤ 5 gross tonnage (GT) or boats without engines or with engine size ≤ 15HP) to have fishing permits (BRKP 2005). As a result, small scale fishing, which accounts for a large proportion of all fishing activities in Indonesia, remains largely unreported (Buchary, pers. comm.). 2.1.2 Study area—Raja Ampat Archipelago The Raja Ampat Archipelago extends over 45000 km2 and includes 4 large islands (Waigeo, Batanta, Salawati and Misool) and around 600 small islands, located adjacent to the northwest tip of the province Papua in eastern Indonesia (Donnelly et al. 2003). The bulk of the catch is caught by small-scale fisheries operating in the reefs and inshore waters (Pitcher et al. 2007) using hook and line, traps, gillnets, lift nets and other methods. A total of 196 species, representing 59 genera and 19 families are classified as target species for reef fisheries in Raja Ampat (Tanda 2002). In 2002, Law no.26 established the new Regency of Raja Ampat (Sumule and Donnelly 2003). A decree by the Bupati (Regent) in 2003 declared Raja Ampat a ‗Kabupaten Bahari‘ (maritime regency) (Conservation International 2008) and consequently aroused interest in fisheries management issues. The aims of this chapter are: i. to estimate the likely range of IUU catch using a semi-quantitative methodology, ii. to reconstruct fisheries catches from the year 1960 to 1994, and iii. to quantify the value of IUU catch from the year 2003 to 2006. 2.2 Methods A ‗semi-quantitative method for Monte Carlo integration of historical sources‘ (MRAG 2005) was applied; the method was originally developed by Pitcher and Watson (2000) to 30 estimate IUU catches for Atlantic Canada. The history of regulatory, technological, political and market changes from 1960 to present were used to estimate trends in the relative rate of misreporting of fisheries catch. The trends were converted to absolute values using anchor points: known rates of misreporting from the literature from the region, and based on expert opinion. A Monte Carlo analysis was used to estimate the likely quantity of IUU catch with associated error ranges. Since its original publication, the methodology has since been further refined and widely used, for example, for fisheries in the North Atlantic (Forrest et al. 2001), Iceland and Morocco (Pitcher et al. 2002), British Columbia, Canada (Ainsworth and Pitcher 2005) and Eritrea (Tesfamichael and Pitcher 2007). The technique has two major advantages: (1) all available data on under-reporting, no matter what regulatory regime is in place, can be combined; and (2) uncertainty of estimates and trends can be addressed by applying a Monte Carlo simulation that uses likely error ranges (ICES 2005). The level of misreporting was analyzed for fisheries targeting reef fish, important pelagics (tuna, anchovy, and shark) and commercial invertebrates (sea cucumber and lobster). After a survey, Erdmann and Pet (2002) reported that the reefs in Raja Ampat were widely impacted by blast fishing, and cyanide fishing and seemed to be a ‗patchwork quilt of damaged and healthy areas‘. The fishery for reef fish was hence divided into illegal catch using destructive methods and unreported catch of fish caught by other gears. Due to the difficulty in dividing up catches of the remaining groups into illegal, unregulated and unreported, a single ‗unreported‘ catch category was used to combine the influence of unreported artisanal fisheries and unreported commercial fisheries (the latter including both catches by local fishers and catches by fishers from outside Raja Ampat). 2.2.1 Catch reconstruction Fisheries catch records for the years from 1994 to 2005 for Raja Ampat were available from the Indonesian Department of Fisheries (Dinas Kelautan dan Perikanan—DKP) (DKP 2007). In 1960, a few hook and line and gleaning fishers operated from canoes in Raja Ampat. As anyone could catch fish by themselves for their own consumption, no 31 local markets existed (Goram 2007). In 1962, the Dutch ceded control of West New Guinea to Indonesia (Cookson 2002), and in May 1963, Indonesia took over the administration of the region (Cookson 2002). From then on, Papua witnessed immigration (locally referred to as ‗Indonesianisation‘) from other provinces of Indonesia (Goram 2007). The immigrant fishers began to catch and sell fish to Java and Sumatra. Papuans were also introduced to fishing with nets (Goram 2007). The increase in population by the influx of immigrants changed the exploitation pattern in Raja Ampat. Catch reconstruction from 1960 to 1993 is based on human population growth rates for 1960-1980 (2.7) from McNicoll (1982) and for 1980 to 1990 (3.41) from Surbakti et al. (2000). For the year 2006, a simple forecast of the catch, equal to the average of the years 2003-2005, was assumed. 2.2.2 Compilation of the influence table An influence table is a chronological documentation of events in the regulatory, technological, political, and economic history of Raja Ampat considered to have influence on the IUU catch for each of the fisheries (i.e., caused an increase or decrease). A significant shift in the exploitation pattern in Raja Ampat occurred after 1960, after the Indonesian government took control over the region. Therefore 1960 was chosen as the starting point of the analysis. Individual events were referred to as ‗influences‘ and assigned numerical ‗IUU influence‘ ratings: (+1) when the influence led to an increase in IUU and (-1) when the influence caused a decline in IUU catches. Each of the influence ratings was weighted by a factor according to the strength of the change it caused to the resource use patterns in Raja Ampat. Six weighting factors were used. Major events at the ‗National‘ (Indonesia), ‗Provincial‘ (Papua) and ‗Local‘ (Raja Ampat) levels were weighted by 1, 3 and 5, respectively. Minor events at the ‗National‘, ‗Provincial‘ and ‗Local‘ level received ratings equal to 0.5, 1 and 2, respectively. Weightings were assigned based on: (i) the poor level of enforcement of existing regulations due to remoteness of Papua from Jakarta, the Indonesian capital city (Hill 1998), and (ii) the assumption that Papuans respond better to decentralized government (Wanandi 2002; Timmer 2005; Bailey 2007) . Most events in the timeline were obtained through interviews conducted by Yohanis Goram (The Nature Conservancy, Sorong, Papua) with 32 local community members (Goram 2007). A list of 150 influences (presented in Appendix C) was considered for the timeline of Raja Ampat‘s fisheries. For each fishery, an influence trend was created by calculating a numerical running total of the weighted IUU influence ratings from 1960 to 2006, where influences likely to have increased IUU fishing were added to the cumulative score, while influences likely to have reduced IUU fishing were subtracted from the same. Figure 2.1 summarizes the influence trends for all fisheries considered. Figure 2.1 Influence trend. The baseline year for the analysis is 1960, hence the influence trend for IUU starts at ‘zero’ for all fisheries except illegal reef fishery, for which the baseline year is 1984. The figure shows the cumulative numerical trend representing relative change in the rate of misreporting versus the time period 19602006. 2.2.3 Quantifying incentive For each fishery, the numerical influence total was divided into five incentive categories: low, low/medium, medium, medium/high, and high. Figure 2.2 illustrates this for the 33 unreported reef fish fishery. The period from 1980 to 2000 showed a sharp increase in unreported reef fish catch. After 2000, the trend reversed slightly. Figure 2.2 Quantifying incentive for unreported reef fish fishery. The influence trend is divided into 5 categories: high (H), medium high (MH), medium (M), medium low (ML) and low (L) 2.2.4 Anchor points The incentive categories were converted into actual catch estimates using anchor points. Anchor points as defined here were absolute estimates of fish catch derived from the literature or from survey information. The details of the anchor points are provided in Appendix C. For incentive categories where anchor points were not available, the absolute catch was obtained using a scaling factor. The scaling factor is based on a rule that the category ‗medium–high‘ represents 80% of the upper cumulative influence total, 34 ‗medium‘ 60%, ‗medium–low‘ 40%, and ‗low‘ 20%. Table 2.1 indicates the absolute range of IUU catch rates for incentive categories of this study‘s selected fisheries. Table 2.1 Absolute estimates for IUU catch ranges. The values in bold are anchor points from literature. The other estimates were calculated using the scaling factor Influence Range Illegal Unreported Tuna level reef Anchovy Shark Sea reef fish Lobster cucumber fish H MH M ML L high 49.06 75.00 61.37 90.35 48.33 59.87 48.25 low 61.32 90.00 76.71 93.78 64.20 97.38 68.79 high 36.62 54.00 54.95 72.28 38.67 47.90 19.35 low 49.06 75.00 61.37 90.35 51.36 59.87 55.03 high 24.53 36.00 30.68 54.21 29.00 35.92 14.51 low 36.62 54.00 54.95 72.28 38.67 47.90 41.27 high 12.26 18.00 15.34 36.14 19.33 23.95 9.67 low 24.53 36.00 30.68 54.21 29.00 35.92 27.52 high 9.81 0.00 0.00 9.67 4.84 low 12.26 18.00 15.34 36.14 18.07 11.97 19.33 23.95 13.76 35 2.2.5 Addressing uncertainty The anchor points provided the range of IUU catch level for each incentive category: low, low/medium, medium, medium/high and high. A Monte Carlo technique was employed to estimate the mean of missing catch with error for each year. The true amount of missing catch (X) would fall somewhere in the estimated range between the lower bound (A) and the upper bound (C) so that, PA X C f ( X )dX 1 c a For values of X between A and C, the probability density function (X) of the triangular distribution is then given by: 2( X A) (C A)( B A) f (X ) 2(C X ) (C A)(C B) if A X B if B X C B is the ‗best guess‘—the mode of the distribution. Figure 2.3 shows the empirical probability distribution. Sampling 5000 times, the Monte Carlo routine empirically determines the mean and 95% confidence intervals. For most of the fisheries, a symmetrical error distribution was assumed, with the most likely missing catch value (the mode) equidistant between maximum and minimum estimates. However, an asymmetric distribution was used for unreported reef fish, in which the mode was represented using a ‗best guess‘ estimate that was shifted to the left of the median value. The asymmetric distribution assumes that the unreported catch might be overestimated by a small amount, but the catch could be potentially underestimated by a large amount. 36 Figure 2.3 Example distribution of the error assumption. Triangular distribution provided for example. (A) lower bound, (B) ‘best guess’, and (C) upper bound. Cumulative probability distribution of missing catch (line). 2.2.6 Quantifying IUU catch revenues in Raja Ampat Regency 2003-2006 The revenue generated from IUU fishing was split into 2 components: (1) revenue from the illegal fishery of reef fish; and (2) revenue from unreported fisheries. 2003 was chosen as the base year for the economic analysis as Raja Ampat started operating as a new regency in 2003 with semi-autonomous government. Fish and shellfish prices were obtained from survey data for the years 2003 (Farid and Anggraeni 2003) and 2006 (Dohar and Anggraeni 2007). Prices were not available for years 2004 and 2005. The consumer price index (CPI) (OECD 2007) for Indonesia, an index used to measure the general rate of inflation (Diewert 2001), was used to convert the nominal 2006 price to real price in 2003 (measured in 2003 US dollars). Prices for years 2004 and 2005 were calculated by interpolating the real price difference between 2003 and 2006 (Diewert 2001). 37 2.3 Results 2.3.1 Catch reconstruction and IUU catch estimation The absolute unreported catch for fisheries operating in Raja Ampat was calculated using the reconstructed catch for the years 1960-2006. Aggregating the results for year 2006 for the reef fish fishery showed that only about 26% of the catch was reported, 20% was caught illegally. Of pelagic species‘ catches, about 43%, 93% and 44% of the catch for tuna, anchovy and shark catches were unreported, respectively. For invertebrates, 42% of the sea cucumber catches and 37% of the lobster catches were unreported. The amount of unreported catch in tonnes in 2006 and the errors associated with individual estimates are shown in Table 2.2. Figure 2.4 shows the reconstructed catches and the trend of reported and unreported catches over the time period 1960-2006. Table 2.2 IUU catch in thousand tonnes in 2006 and the error on the estimates. Catch and error estimate Reported catch ('000 tonnes) Unreported catch ('000 tonnes) Error% (-ve) Error% (+ve) Illegal reef fish 4.054 Other reef fish 4.054 Tuna Anchovy Shark Sea Lobster cucumber 17.626 1.321 0.598 0.017 0.630 3.043 8.205 13.233 15.339 0.460 0.012 0.371 18.6 21.7 34.3 38.2 33.2 45.7 15.5 21.2 14.4 16.3 17.5 21.2 48.4 75.4 38 Figure 2.4 Reported and unreported catches in Raja Ampat. The dark grey is the reported catch; the catches prior to 1990 are the results of reconstruction. The light grey is the unreported catch. In the first graph the black area represents illegal fishery for reef fishes. 2.3.2 Quantifying the IUU catch revenues in Raja Ampat Regency 2003-2006 Results from a comparison of total revenue generated in the period 2003-2006 from reported versus illegal and unreported catch are shown in Figure 2.5. The error associated with individual estimates is not shown in the graph but is included in Table 2.3. The results show that over the four year period, revenue from the IUU catch in Raja Ampat totalled 160 million US dollars (in 2003 USD), or an average of 40 million USD a year. 39 Table 2.3 Total revenue from IUU fishing for 2003-2006 and error on the estimates Value of catch and error Catch value (million USD) Error% (-ve) Error% (+ve) Illegal Reef fish 42.4 Other Reef fish 16.1 Tuna 50.4 Anchovy Shark 18.5 7.6 Sea Cucumber Lobster 0.1 25.2 18.3 21.7 34.5 38.1 19.0 24.6 15.5 21.8 17.2 20.9 14.3 16.3 48.5 76.1 Figure 2.5 Total revenue from IUU fishing in Raja Ampat (2003-2006). The light grey bars are the revenue generated from reported catches, the black bars are the revenue from unreported catches. The shaded bar in category reef fish is the revenue from illegal fishing. 40 2.4 Discussion 2.4.1 Catch reconstruction Since 1963, Raja Ampat and other areas in Papua have experienced an increasing influence from the central government: new schools have been established and new development projects undertaken and there has been an influx of administrators, businessmen, and security forces from the other provinces of Indonesia (Goram 2007). The population influx and technological advances led to a shift away from a predominantly subsistence based lifestyle to one based on cash crops and extractive industries such as mining, logging (WWF/IUCN 1996) and fishing for commercial purposes (Palomares and Heymans 2006). Important factors that contributed to the change in marine exploitation patterns were the significant surge in population size and demand for sea cucumber, pearls and sea turtles from the Raja Ampat Archipelago (Palomares and Heymans 2006). 2.4.2 Estimation of IUU fishing in Raja Ampat 220.127.116.11 Reef-fish fisheries For the purpose of clarity, the results for the reef-fish fisheries are described under two categories: the illegal catch and the unreported catch of reef fish. Illegal fishery Cyanide fishing began in a limited capacity by fishers from outside Raja Ampat in the early 1980s and became very popular by the mid-1980s (Goram 2007). Indonesia began exporting live reef fish to Hong Kong in 1988 (Chan 2000a). The expanding market for live reef fish fuelled over-exploitation. By the mid-1990s, Indonesia accounted for about half of the live fish supply in the markets of Hong Kong and Singapore (Johannes and Riepen 1995). By the late 1990s, fishers in several parts of Indonesia were experiencing a decline in target fish in shallow waters; cyanide fishermen reported declines in catch per unit effort of up to 90% in the latter half of the 1990s (Chan 2000b). Similar to the trend experienced in the other parts of Indonesia, fishers in Raja Ampat also experienced 41 declines in large groupers and Napoleon wrasse (Cheilinus undulates) (Goram 2007). However, hope of better catches in Eastern Indonesia caused further influx of more fishers into Raja Ampat and in the early 2000s, mouse grouper (Cromileptes altivelis) and Napoleon wrasse had also become scarce in Raja Ampat. Blast fishing in Raja Ampat was introduced by fishers from Buton, Sulawesi and Biak (an island located in Cenderawasih Bay close to the northern coast of Papua). It started on Crocodile Island (a small island close to Sorong) in the late 1980s (Kadarusman unpublished document). Fishers in Raja Ampat, especially the younger generation were encouraged to adopt destructive fishing methods when they observed the high profits made by fishers from outside Raja Ampat (Goram 2007). This shift that happened in late 1980s was locally recognized as a viable option because of increased competition from Sulawesi fishers fishing for marine invertebrates (Goram 2007). The associated ‗macho‘ status and favorable response from the opposite sex was a bonus. It was not difficult to adapt to this fishing method because fishers from Sulawesi were supplying bombing material in Sorong (Goram 2007; Kadarusman unpublished document). Villagers who chose to participate in the cyanide fishery were supplied with boats and all necessary equipment (Sumule and Donnelly 2003). Live fish buyers from Sorong offered fishers a large down payment in return for sole purchasing rights to the fishermen‘s catch. The ‗exclusive buyer‘ often forced fishers to fish heavily every day in order to clear his debt (Sumule and Donnelly 2003). The rise in the number of fishers engaged in blast fishing (Goram 2007), and the supply of bombing material from Sulawesi (Kadarusman unpublished document) and East Java (Goram 2007) caused widespread destruction of reefs around Raja Ampat. Villagers in Waigeo stated that they heard blasts almost daily (Bailey, M.10 pers. comm.)—a situation corroborated by villagers in Kofiau. The actual level of blast fishing remains difficult to quantify as recent aerial surveys failed to observe any active operations (Ainsworth et al. 2008). 10 Megan Bailey, University of British Columbia 42 Illegal fish catches in Raja Ampat peaked in the late 1990s and early 2000s (Goram 2007) (as can be observed in Figure 2.4). Live fish transport vessels from Hong Kong periodically collected fish from major karambas (floating net cages for holding live fish); Indonesian military personnel were usually on board the transport vessels hinting at the ‗collusion between the outside syndicates and military officials‘(Sumule and Donnelly 2003). Willoughby et al. (1999) recognized the difficulty in controlling the transshipment of large numbers of illegally caught and unreported fish. Fishers from outside Raja Ampat trans-shipped their catch to the ships from Hong Kong or landed at unofficial landing sites outside Raja Ampat (Suebo, A.11 pers. comm). Hence, chances for illegal catch being recorded in the official Indonesian catch statistics were minimal. In the early 2000s, as large reef fish were getting scarce, fishers began targeting small reef fish to supply the ornamental fish trade (Goram 2007). The relative scarcity of large reef predators, particularly the absence of males, was also recorded during resource evaluation assessment of coral reefs in Raja Ampat (Donnelly et al. 2003) in 2002. Scarcity of breeding males is recognized to undermine the viability of spawning aggregations (Donnelly et al. 2003). Overfishing has been previously implicated in the disappearance of spawning aggregations (Colin 1992; Aguilar-Perera and Aguilar-Dávila 1996; Domeier and Colin 1997; Johannes et al. 1999). Several conservation minded non-governmental organizations (NGOs) arrived in Raja Ampat in the period 2000-2005; since then, they have considerably increased public awareness of the destructive effects of cyanide and blast fishing. In fact, many Raja Ampat fishers have stopped blast and cyanide fishing and shifted to longlines and gillnets as a result of awareness campaigns launched by the Nature Conservancy (Pastor Mambrasar and Pastor Katutun12 pers. comm.). Fishers reported higher catches in pelagic species following the self-imposed ban on use of destructive fishing methods (Pastor Katutun pers. comm.). Today, local fishers who engage in blast fishing are despised by village chiefs and local elders (Pastor Mambrasar pers. comm.). Homilies at local churches in rural communities such as Kofiau are pro-conservation in their message and 11 12 Anton Suebo, The Nature Conservancy, Bali, Indonesia Pastor Membrasar and Pastor Katutun, Religious priests, Raja Ampat, Indonesia 43 this has inspired villagers to support the implementation of large (>4700 km²) marine protected areas (MPAs) where local fishermen and the Raja Ampat Regency government might be able to prevent access by destructive fishers from other parts of Indonesia. E.g., the villagers celebrated with enthusiasm the setting up of an MPA in Kofiau Island (Ainsworth and Varkey 2007). Unreported reef fish fishery The hook and line fishery is the most important fishery for reef fish in Raja Ampat (Ainsworth et al. 2007). The high quality live fish are sold to fishers owning karambas, who sell it to local live reef fish traders or to ships from Hong Kong (Donnelly et al. 2003). The remainder of reef fish catch is sold in the local market. The landing center in Sorong is more than 100 miles away from Kofiau and Misool Islands in Raja Ampat. The price of fish is higher in Sorong market (Rotinsulu13, pers. comm.), but the incentive is not worth the cost in terms of fuel, time and travel. Misool island has larger number of villages (20), compared to 3 villages in Kofiau (Djuang and Imbir 2007). The catch has good demand from employees of pearl farms in Misool. A large number of residents of Misool Island are descendants of immigrants from Sulawesi, and still maintain strong ties. The catch landed in Misool is often dried and traded in Sulawesi markets (Suebo, A. pers. comm.). To the east of Misool Island, on the west coast of mainland Papua, is the city Seram (not part of Raja Ampat Regency). Fishers often obtain fuel for their boats in Seram and trade their fisheries catch there (Suebo A. pers. comm.). As these smaller landing sites do not keep records, a large proportion of the overall catch does not enter the official Indonesian statistics. The Raja Ampat Regency needs better methods to quantify the catches from the islands which are far from Sorong. 18.104.22.168 Tuna fisheries In 1967-1969, two boats (Injeros and Cakalang) operated by local government company (PD. Irian Bakti) began fishing for tuna under the control of Fisheries Department in Sorong (Goram 2007). In 1973-1975, two companies––Usaha Mina and PT. Alfa 13 Chris Rotinsulu, Conservation International, Raja Ampat, Indonesia 44 Kurnia––started fishing for tuna in Raja Ampat waters (Goram 2007). Up to 80 boats of 50 tonnage capacity operated in this time. Another tuna company PT. Ramoi started its operation in 1982-1984; the peak period of tuna fishing was in 1994-1996 (Goram 2007) The Fisheries Department (DKP) reported that in 2005 the commercial catch of tuna from Raja Ampat was approximately 369 tonnes. However, information collected during the Conservation International valuation study (Dohar and Anggraeni 2007) showed that the catches from two companies (PT. Radios Apirja Sorong and KUD Tuna Cakalang Tunas Jaya) fishing in Raja Ampat and adjacent waters alone exceeded the reported catch from Raja Ampat (819.16 tonnes). There was anonymous information that the tuna industries based in Sorong severely under-reported their tuna catch. In spite of considerable effort (Rotinsulu pers. comm.) no data could be collected on the true tuna catches. Given the financial incentive to under-report (reduced taxes), the secrecy raises concerns over the true levels of under-reporting. Willoughby et al. (1999) reported a similar situation with tuna catch reports; he stated that total tuna declarations were probably little more than half the actual catches. This chapter explores only the level of unreported fishing for tuna in Raja Ampat, estimating the illegal fishing for tuna by foreign vessels will raise the estimate of tuna catches higher. 22.214.171.124 Anchovy fisheries An anchovy fishery in Kaboei bay fishery on Waigeo Island has been recognized as being feasible since 1954 because catches up to 1 tonne per hour in shallow coastal areas could be achieved (Palomares and Heymans 2006). An artisanal lift net raft provided with a small shelter ‗bagan‘, is most commonly used for anchovy fisheries in Raja Ampat. The Waigeo fishery began in 1973-1975 by migrant fishers from South Sulawesi (Goram 2007). Bailey et al. (2008) estimated of 49-76 tonnes annual catch of anchovy per bagan. The total number of bagans fishing in Raja Ampat was based on sightings during an aerial survey of Raja Ampat (Barmawi 2006). The migrant anchovy fishers are not required to report their catch, and it is trans-shipped at sea to Java, Western Indonesia. The migrant fishers earn almost twice as much as an average fisherman from Raja Ampat (Bailey et al. 2008). Monitoring the migrant anchovy fishery is an important 45 consideration for the fisheries management program in Raja Ampat Regency (Bailey et al. 2008). 126.96.36.199 Shark fisheries Fishing for shark fin became very popular from 1976 to 1981. Local fishers often found bodies of shark with fins cut off in the coastal areas. Nets more than 2 km long were used by fishers from Madura, East Java; Selayar and Buton, South Sulawesi. But by 19901993 fishers had started experiencing difficulty in locating shark (Goram 2007). Farid and Anggraeni (2003) reported that in 2000-2002 shark fin collectors in Sorong gave 8 to 10 million Rp. per trip to the fishers from outside Raja Ampat to catch sharks, equivalent to approximately $900 to $1000 USD (1USD~9000 Indonesian Rupiah). Allen (2003) attributed the ‗paucity‘ of reef sharks in Raja Ampat to the shark fin trade. In 2002, there were reports that villages in Kapadiri, Waigeo Island cooperated with fishing companies from the Philippines. The companies paid an access fee of Rp 500,000 (=~55$USD) to the village and provided fishers with generators and outboard motors. Depending on the quality of the shark fin, the company paid the fishers Rp 1,800,000 to Rp 3,000,000 (200300 $USD) per kilogram of dried shark fin. The fishers landed carcasses of small and medium-sized sharks for consumption, but the larger sharks were discarded (Donnelly et al. 2003). More than 100 boats (about 7m long) from Halmahera (islands west of Raja Ampat) currently fish for sharks in Raja Ampat. The shark fin catch is trans-shipped to Halmahera or Makassar and then to Japan. All the fishers on the vessels are Indonesian; however, the investment for the vessels comes from outside Indonesia (Suebo, A. pers. comm.). The local fishers and live aboard operators suggest there has been a great decline in the shark population in Raja Ampat (Djuang14 pers. comm.). In recent years, the number of shark fishers have decreased; however, the fishers who remained in business have started targeting manta ray aggregations near Wayag and Sayang Islands (two islands Southwest of Waigeo Island) in Raja Ampat (Suebo, A. pers. comm.). The price data (Farid and 14 Jacinta Djuang, Conservation International, Raja Ampat, Indonesia 46 Anggraeni 2003; Dohar and Anggraeni 2007) showed a dip in the prices of shark fin from 2003 to 2006. The probable reason for the dip is that the prices reported in 2003 were for only 2 species (black shark Charcarhinus melanopterus and lontar sharks Isurus glaucus) and those species have declined considerably over the years. The price reported in 2006 is for an assorted group of shark fins. 188.8.131.52 Sea cucumber fisheries Though the total revenue from sea cucumber fisheries is low, a large number of fishers in Raja Ampat engage in gleaning to catch them. Sea cucumber fetches a very high price in the market, but the catch is small compared to the other fisheries analyzed in this chapter. Between 1928 and 1933 both Trochus shells and sea cucumber were exported from Sorong and Misool (Palomares and Heymans 2006). By 1934-1935 the export of sea cucumber from the territory of Papua was 40 tonnes per year. Sea cucumber remained an important export item in 1954 (Palomares and Heymans 2006). Commercial extraction of invertebrates, e.g., mollusc shells and sea cucumber, continued in spite of signs of overexploitation (Palomares and Heymans 2006; Palomares et al. 2007). Fishers from South Sulawesi began fishing for sea cucumber in ‗large scale‘ in Raja Ampat in 1978 and fishers began observing a decline in the sea cucumber population in late 1990s (Goram 2007). 184.108.40.206 Lobster fisheries A resource use survey in 2007 (Muljadi unpublished data) observed lobster catch on 5 out of 10 vessels from Sulawesi. All these vessels operated with inboard engines and fished using compressors. Lobsters are an important catch for local fishers; crustacean catches account for about 13% of total catch and is mainly composed of lobsters and shrimp (Muljadi 2004). In 2003, the price for lobster ranged from Rp 35000 (4 $USD)/kg (for baby size 0.2–0.5 kg) to Rp 115000 (13$USD)/kg (for super-size 0.8–1.2 kg) (Farid and Anggraeni 2003). The super-size was caught by fishers who used compressors for diving. In 2006, the average price for lobster in Sorong market declined to Rp 32,500 (3.5 $USD)/kg (Dohar and Anggraeni 2007). The decline is probably due to the fact that the average size of the lobster has declined over the years due to overexploitation. Since the 47 majority of the fishers in Raja Ampat do not use compressors, they are not able to catch big lobsters and fetch higher prices. Fishers from outside Raja Ampat who have motorized vessels and use compressors would fetch higher price than fishers in Raja Ampat, but their catch is not landed in Sorong. 2.4.3 Quantifying the economics of IUU catch in Raja Ampat 2003-2006 In Raja Ampat Regency, sustainability of marine resources is important to the economy and food security. Throughout the year, most Regency inhabitants are involved in subsistence fishing, even though they may be employed in other industries as their main revenue sources (farming, construction, pearl farming, etc.) (Bailey et al. 2008). The estimated revenue generated by illegal fishing of reef fish is almost equal to the revenue from all reef fish catch in Raja Ampat (reported and unreported combined). Until the late 1990s, almost 90% of the grouper and Napoleon wrasse are caught by fishers from outside Raja Ampat while the local fishers caught about 90% of the other reef fishes (Erdmann, M.15 pers. comm.). The grouper and Napolean wrasse fetch very high price in the market (~50 000 Rp/kg = USD 5.5/kg) compared to other reef fish (~7000 Rp/kg = <1 USD/kg). The resource is hence being exploited but with little gain to the local fishers. There was anecdotal information of serious under-reporting by tuna companies: a conservative estimate of $50 million USD over the four year period 2003-2006 was arrived at. At a tax rate of 2.5%, the government likely lost revenues of over $1 million USD in 4 years. Fishers and fishing companies fishing for anchovy and shark paid the villages a one-time small access fee to fish in their waters (Donnelly et al. 2003; Bailey et al. 2008). The revenue generated from unreported anchovy and shark fishing in the 4 year period 2003-2006 was over $18 million and $7 million USD, respectively. Centralization of the access system to Raja Ampat waters could turn these fisheries into a profitable enterprise for the regency. The hook and line fishery is the most important fishery for reef fish in Raja Ampat (Ainsworth et al. 2007) and the unreported reef fish fishery accounts for about $16 million USD. Most of the fisheries are small scale and do not contribute to government revenue in the form of taxes. However, the amount of catch and revenues is 15 Dr. Mark Erdmann, Conservation International, Bali, Indonesia 48 indicative of the economic status of the average Raja Ampat fisher and serves as a guide for deciding the trade-off between the monitoring expenditure and expected revenue. 2.5 Conclusion The marine species diversity in Raja Ampat is one of the highest in the ‗coral triangle‘ (McKenna et al. 2002). However, Diamond (1986) noted in that marine resources in Raja Ampat were probably overfished. Subsistence or traditional fishing vessels are not required to have fishing permits (BRKP 2005); this is the reason why overexploitation and under-reporting by small scale vessels has received little attention compared to illegal fishing by foreign vessels in Indonesian waters. The indigenous people of Raja Ampat are rapidly being integrated into the cash economy and moving away from subsistence to commercial exploitation (Sumule and Donnelly 2003). After being declared as a ‗Maritime Regency‘, it is the mandate of the regency to improve the marine management system. To this end, the fisheries department in Raja Ampat is conducting an inventory of the fishing vessels operating in Raja Ampat (Rahwarin, B. pers. comm.). Seven protected areas of total size of 4700 km-2 were declared in Raja Ampat in 2006 (Rabu 2006). For better reporting of the fisheries in Raja Ampat, it is necessary to setup catch recording booths in the major fishing villages that would report to the Raja Ampat Regency fisheries department office. The true extraction of fish and shellfish from the coral reefs is essential to plan for future management policies, for example––control of access in Raja Ampat waters, improvement of data collection mechanisms, control of illegal fisheries. The fishers in Raja Ampat had traditional marine tenure which declined in importance after integration into Indonesia. It was disillusionment over unregulated access and the belief that their paradise was being plundered that led young fishers to engage in destructive fishing (Halim et al. 2007). Estimates of the true catch and its effect on local ecosystems and economies should encourage a restructuring of marine management. This may lead Raja Ampat on a path towards sustainable resource exploitation. 49 2.6 References Agnew D., J. Pearce, T. Peatman, T. J. Pitcher, and G. Pramod. 2008. The global extent of illegal fishing. Marine Resources Assessment Group. London, UK. Available: < www.mrag.co.uk/Documents/ExtentGlobalIllegalFishing.pdf > [Accessed on February 12, 2009] Aguilar-Perera A., W. Aguilar-Dávila. 1996. A spawning aggregation of Nassau grouper Epinephelus striatus (Pisces: Serranidae) in the Mexican Caribbean. Environmental Biology of Fishes 45(4):351-361. Ainsworth C. 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Appendix I Marine Conservation. 55 3 Ecological Restoration and Ideal Minimum Size of No-Take Zones in Marine Protected Areas of Raja Ampat, Indonesia16 3.1 Introduction 3.1.1 MPA for ecosystem based management In the view of global declines of target and non-target marine fish and invertebrates (Alverson 1994; Pauly et al. 1998; Hutchings and Reynolds 2004), management emphasis has shifted towards integrated ecosystem approaches and a variety of nomenclature has evolved around this shift in management focus. Ecosystem approaches have been adopted under several names: Ecosystem approach to fisheries (EAF) by the UN Food and Agriculture Organization, Ecosystem based fisheries management (EBFM) by the US National Marine Fisheries Service and Ecosystem based management (EBM). Though based on the same concept, the difference in nomenclature represents some differences in operation. EAF is an overarching concept in that ―it is not limited to management but could also include development, planning etc‖ (NMFS 1999; Garcia et al. 2003). EBM is management based on ecosystem approaches and can be interpreted correctly to mean several aspects of management, for example, impact of pollution on coral reefs to studying fisher poverty and its influence on resource use. EBFM is a more precise approach where the focus is on making decisions for the management of a resource (species or groups of species) based on understanding their roles and interrelationships in the ecosystem (NMFS 1999). Marine protected areas (MPAs) may 16 A version of this chapter has been submitted for publication. Varkey, D. A., Ainsworth, C. H., and Pitcher, T. J. Ecological restoration and ideal minimum size of no-take zones in marine protected areas of Raja Ampat, Indonesia. 56 offer an important tool to reduce fishing mortality, mediate habitat damage, increase stock biomass, and preserve ecosystem biodiversity (Gell and Roberts 2003; Hooker and Gerber 2004). Establishing MPAs may provide managers the opportunity to achieve EBM (Halpern et al. 2010) by addressing biological concerns and socio-economic needs (Sumaila et al. 2000), both of which are integral components of EBM. Review of 89 empirical results of marine reserves shows that on average the density, biomass, diversity and size of organisms are higher inside the reserves (Halpern 2003). Reserves appeared to promote an increased density of exploitable fishes in reef ecosystems in Philippines (Russ and Alcala 2003; Alcala and Russ 2006), and in the Caribbean (Bartholomew et al. 2008; Schrope 2008). 3.1.2 Raja Ampat The Raja Ampat archipelago, consisting of approximately 610 islands, is located in the Southeast Asian Coral Triangle. The area extends over 45,000 km2 and encompasses a variety of marine habitats including some of the most biodiverse coral reef areas on Earth (McKenna et al. 2002; Donnelly et al. 2003). The name Raja Ampat (four kings) refers to the four major islands (Figure 3.1)— Batanta, Misool, Salawati, and Waigeo (Donnelly et al. 2003). Small-scale fisheries operations on the reefs and in the inshore areas provide livelihoods for around 24,000 fishers (Dohar and Anggraeni 2007). Modeling work (Ainsworth et al. 2008a) and analysis of fisher perceptions (Ainsworth et al. 2008b) show that fishing pressure on the resources has caused the decline of several exploited species. In 2002, Law no. 26 established the new Regency of Raja Ampat, and in 2003 a decree by the Bupati (Regent) declared Raja Ampat a ‗Kabupaten Bahari‘ (maritime regency) (Conservation International 2008.). These political changes helped to establish a new network of marine reserves in 2006. The network covers a total of 4793 km2 of sea area and 44% of reef area in Raja Ampat. It includes seven MPAs in the Islands: Ayau (28 km2), Southwest Waigeo (162 km2), Sayang-Wayag (178 km2), South Waigeo or Dampier Strait (202 km2), Mayalibit (277 km2), Kofiau (328 km2) and Southeast Misool (943 km2). Ecological changes in three (Kofiau, Southeast Misool and Dampier Strait) MPAs were investigated. 57 Batanta Salawati Figure 3.1 Map of Raja Ampat The map shows the location of the Raja Ampat model (full map area) and the sub-area models within the Raja Ampat map (Dampier, Kofiau and Misool). The areas drawn in bold are the official MPA areas. (The figure is reproduced from Ainsworth et al. 2007). 3.1.3 Birds Head Seascape ecosystem based management project and spatial ecosystem based management research interests Concerned with the issues of fisheries management and with the intention to develop environmentally sound ecosystem based policies, the Regency government participated in 58 a science-based initiative—the Birds Head Seascape17 Ecosystem Based Management (BHS EBM) project—funded by the David and Lucille Packard Foundation. The project involved field study and ecological modeling with The Nature Conservancy (TNC), Conservation International (CI), World Wildlife Fund (WWF) and the University of British Columbia (UBC). The following research questions focused on increased species biomass and MPA zoning options were identified during discussions with the Raja Ampat Fisheries Office and the partner institutions in the project. i. Determine conservation benefits of restricting fishing effort inside MPA ii. Determine conservation benefits of a single large versus several small MPAs 3.2 Methods 3.2.1 Ecopath with Ecosim and Ecospace Ecopath with Ecosim (EwE) modeling approach was used to build the coral reef ecosystem model, and Ecospace for spatial analysis of MPAs. The details of EwE model parameterization can be found in Appendix B. EwE is a mass balance trophic simulation model that acts as a thermodynamic accounting system for marine ecosystems. Ecopath is a static snapshot of the system (Christensen 1992) that maps the thermodynamic flows in the system. Ecosim allows modeling of species composition changes over time (Walters et al. 1997); finally Ecospace integrates Ecopath and Ecosim across a two dimensional spatially explicit domain (Walters et al. 1998; Pauly et al. 2000). In Ecospace, a regular grid, which represents the study area, is divided into a number of habitat types. Each functional group is allocated to its appropriate habitat(s). Each cell hosts its own Ecosim simulation and is linked through symmetrical biomass flux in four directions. The exchange rate of biomass between the cells is determined mainly by dispersal rates in combination with the habitat type in adjacent cell, and group foraging and predator avoidance behaviour (Walters et al. 1998). Optimal and sub-optimal habitat in adjacent 17 Bird Head Seascape is located in northwest Papua, Indonesia, it extends from Raja Ampat archipelago in the west to Cenderawasih Bay in the east and FakFak-Kaimana coastline in the south (http://www.conservation.org/sites/marine/initiatives/seascapes/birds_head/Pages/birdshead.aspx) 59 cell can be distinguished using parameters such as the availability of food, vulnerability to predation, and immigration/emigration rate. Dispersal rates represent net residual movement of functional groups on an annual basis and are not related to swimming speeds (see Walters et al. (1998) for more details). Details of the dispersal rates used in the model are provided in Appendix D. The effects of MPAs can be explored, and hypotheses regarding ecological function and effects of fisheries can be tested by delimiting an area as a protected zone in the Ecospace model. Previous authors have used Ecospace in this capacity (Walters et al. 1998; Pitcher and Buchary 2002; Salomon et al. 2002; Jiang et al. 2008; Le Quesne and Codling 2009). 3.2.2 Ecospace models used in the analysis EwE models of Raja Ampat were built by integrating data from extensive field studies. Fisheries catch data for the same model were assembled from records of the the Sorong Regency Fisheries Office (Departemen Kelautan dan Perikanan, DKP), the Raja Ampat Regency Fisheries Office, and the Trade and Industry Office (Departemen Perinustrian dan Perdagangan). For greater detail on Ecopath model parameters and Ecosim fitting to time series, interested readers are referred to online technical reports (Ainsworth et al. 2007) and (Ainsworth et al. 2008c) (see Appendix F). This chapter explores marine protected areas using Ecospace models for Raja Ampat. Raja Ampat Ecospace model was used for the analysis of the first research question. The Ecospace model inherited the standard EwE parameters from the 2005 Raja Ampat model. The model was used to compare the effects of restricting fisheries in three of the seven MPAs declared in Raja Ampat (Kofiau Island, Southeast Misool Island and Dampier Strait). The habitat maps were created by utilizing GIS information assembled by the BHS EBM project (Barmawi, M18. pers. comm.). For the analysis of the second research question, the 2005 Raja Ampat model was adapted to build higher resolution Ecospace models for the same MPA areas analyzed in 18 M. Barmawi TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228. unpublished data. Contact: email@example.com.) 60 the first research question. The higher resolution models (Figure 3.1); hereafter referred to as the sub-area models, improved the spatiotemporal representation and allowed us to simulate natural predator-prey segregation. The details for creation of sub-area models can be found in Appendix E. In our previous publication (Ainsworth et al. 2008a presented in Appendix F), the results of dynamic Ecosim simulations for Raja Ampat were synthesized. 3.2.3 Ecosystem effects of restricting fisheries inside the MPAs The following paragraphs describe the three types of fishing restrictions employed in the Raja Ampat Ecospace model. At the end of 20-year simulations, the changes in biomass and catch for reef fish inside the MPAs and catch in the spillover regions (cells adjacent to the MPAs) were examined. 220.127.116.11 No fishing allowed (no-take) In the Raja Ampat Ecospace model, all fisheries from inside the MPAs were eliminated to examine ecosystem recovery. 18.104.22.168 Commercial fisheries restricted (artisanal fisheries allowed) The following fisheries were assumed to be commercial: driftnet, diving for live fish, diving with cyanide, blast fishing, trolling, purse seine, and pole and line. The other gear types were assumed to be primarily artisanal: spear and harpoon, reef gleaning, shore gillnets, permanent trap, portable trap, diving with spear, and set line. The distinction between artisanal and commercial catch is difficult to draw due to the unreported and unregulated nature of Raja Ampat reef fish fisheries and widespread casual local trade. The gear types were chosen to highlight the distinction between fishing sectors that require low capital investment, and/or whose products are destined for a small-scale local market; versus fishing sectors that require high capital investment and/or whose products are destined for regional or international market. Capital-intensive fishing methods such as compressor diving and fisheries that produce a high value product suitable for export, such as cyanide fishing, were assumed to be commercial. Blast fishing provides a high yield of low-value product that is likely to be absorbed by a large regional market, and so 61 this fishery was assumed to be commercial. In this scenario, the above stated commercial fisheries were eliminated from inside the MPA in the Raja Ampat Ecospace model. 22.214.171.124 Destructive (blast fishing and cyanide) fisheries restricted Destructive fishing practices, cyanide fishing and blast fishing, are widely prevalent in Eastern Indonesia (Erdmann and Pet-Soede 1996; Edinger et al. 1998) and are recognized as serious threats to coral reef ecosystems (Erdmann 2000; Fox et al. 2003). One of the major goals of the declared MPAs in Raja Ampat was to restrict the entry of fishers, especially fishers from outside Raja Ampat, engaged in destructive fishing. In this scenario, destructive fishing methods (cyanide fishing and blast fishing) were eliminated from inside the MPAs to examine recovery. 3.2.4 Ecological benefits of single large versus several small MPAs In each of the sub-area models, 8 combinations of MPA sizes were analyzed. The total area protected was set equal to approximately 100 km2 in all the scenarios. The total protected area was divided into combinations of 1, 2, 4 6, 8, 10, 20 and 30 MPAs (100km2*1, 50*2, 25*4, 16.67*6, 12.5*8, 10*10, 5*20 and 3.3*30). To control the uncertainty from non-random siting of MPAs of different sizes, it was ensured that the multiple MPAs had similar amounts of reef habitat and the MPAs were located roughly evenly along the coast. It is also expected that as the number of MPAs increased, concern from non-random placement of MPAs became less as more values were averaged. The same pattern of closure was followed in all the sub area models to see if similar results would be obtained in the replications (see Figure 3.2 for an example of the closure patterns). At the end of the 20 year simulation run, the relative differences in reef fish biomass density between the various MPA sizes were analyzed. 62 13 * 13 cells Figure 3.2 Example of the closure patterns in Kofiau Ecospace model. The MPAs are indicated by the grey cells in the map. In all the closure scenarios the total area closed remains the same. 63 3.3 Results 3.3.1 Ecosystem effects of restricting fisheries inside the MPAs (Research question-1) 126.96.36.199 Reef fish biomass inside MPA For the purpose of summarizing the results, the reef fish species in the Raja Ampat Ecospace model were aggregated into 3 categories: large reef fish, medium reef fish and small reef fish. In all three MPAs, the biomass of large reef fish was at least two times higher (Kofiau 2.3, Dampier 2.6 and Misool 3.1) when no fishing was allowed in the MPAs (Figure 3.3). Under restriction of commercial fisheries, rebuilding of the large reef fish populations was modest; a definite increase was observed only when no fishing was allowed (Kofiau 67% increase, Misool 92% and Dampier 112%). When status quo fishing was continued, the biomass density of large and medium fish decreased relative to model initialization conditions in all the MPAs suggesting that current levels of fishing will lead to further declines in the biomass of target species. A trophic cascade was evident in all the MPAs. In response to increased biomass of large reef fish, the biomass of medium reef fish decreased; thereby, releasing the small reef fish from predation; this is consistent with known ecology (Carpenter and Kitchell 1996). Medium reef fish increased above their base levels only in the Dampier MPA in the ‗no fishing‘ scenario. Compared to the ‗status quo‘ scenario, the decline in medium reef fish was lower under fishing restriction scenarios, but the benefits of the MPAs were dampened by increased predation pressure from large reef fish. In the Misool Island and Dampier Strait MPAs, the highest increase in small reef fish (~140%) occurred when all commercial fishing was restricted. When all fishing was closed, the increased predation pressure caused a decrease in the biomass of small reef fish in Dampier (5%) and Misool (24%). Disallowing destructive fishing alone was not sufficient to ensure rebuilding of the large and medium reef fish from their baseline biomass levels. The response of small reef fish in the Misool and Dampier Strait MPAs was strongest. In all the MPAs, reef fish 64 benefited relative to the status quo scenario. Compared with restricting commercial fisheries, restricting destructive fisheries had similar pattern but smaller magnitude. Figure 3.3 Relative biomass changes inside MPAs. The graphs show the change in biomass relative to the base (2005) biomass of large, medium and small reef fish. The fishing restriction scenarios are shown on the horizontal axis: NF-No fishing, NC-No commercial, ND-No destructive and SQ-Status Quo. Black bars represent large reef fish, white bars represent medium reef fish, and grey bars represent small reef fish. Ecospace model results were sensitive to dispersal rates (Figure 3.4). This is probably because species with higher dispersal rates, such as highly mobile pelagic fish, suffered fishing mortality from outside of the reserve. Prominent rebuilding effects were seen in 65 species with low dispersal rates (<30 km.yr-1), especially in the lower trophic level functional groups. At higher trophic levels, dispersal rates did not seem to influence the amount of rebuilding significantly. In the trophic level range from 2.5 to 3, highest level of rebuilding was shown by some functional groups with the lowest dispersal rates; however the response varied widely in this category especially because juveniles of several functional groups belonged in this category. Figure 3.4 Influence of dispersal rate on biomass density change. The relative change in biomass of functional groups obtained in the MPAs under no fishing scenario were combined and grouped into 5 classes according to the dispersal rates. Box plots are drawn to show the range in biomass change. The 5 classes of dispersal rates are shown on the horizontal axis. 66 188.8.131.52 Reef fish catch inside MPA and in spillover regions Under status quo, the decrease in predator biomass caused a subsequent increase in biomass and catch of small reef fish in Misool and Dampier MPAs (Figure 3.5). Yield of large reef fish (Misool 49%, Dampier 24%), and medium reef fish (Misool 41%, Dampier 43%) decreased. The response in the Kofiau MPA was different; the catch of large, medium and small reef fish declined from the base levels in all the scenarios. Catch of large, medium and small reef fish increased in the spillover region around Kofiau MPA. However, the catch from the spillover regions in Misool and Dampier did not increase between the status quo and fishing restriction scenarios. Catch inside the MPAs explained the difference in spillover catch in the Kofiau MPA versus Misool and Dampier MPAs. Increase in catch was observed only in Dampier Strait and Misool MPAs and not in Kofiau MPA. Alternatively, when the fishing was high inside the Kofiau spillover region, catches inside the MPA were not high. The results indicated a tradeoff between allowing some fisheries to operate inside the MPA versus expecting spillover effects from the MPA. 67 Figure 3.5 Relative catch changes inside MPA The bars show the change in catch relative to the base (model initialization for 2005) catch of large, medium and small reef fish inside the MPAs under fishing restriction scenarios shown on horizontal axis: NC-No commercial, ND-No destructive and SQ-Status Quo. Black bars represent large reef fish, white bars represent medium reef fish, and grey bars represent small reef fish. 3.3.2 Ecological benefits of single large versus several small MPAs The biomass density of large, medium and small reef fish increased within the protected areas as the size of MPA increased in Kofiau and Misool (Figure 3.6). However, benefits from the MPAs reached an asymptote as the size of the no-take area increased. Beyond about 16 km2 (Kofiau) and 25 km2 (Misool), there was no additional benefit in biomass density as the size of the MPA increased. The results from the Dampier Strait Ecospace model were opposite to the response seen in Kofiau and Misool, with the largest MPA showing the smallest values of biomass density for reef fish. The magnitude of response in the Misool model was higher than the response observed in the Kofiau model, but the 68 absolute values at which the biomass density responses levelled off were very similar. The scenarios were repeated with default values for dispersal rate in Ecospace (300 km.yr-1 for all the functional groups). Overall, the performance of all the MPAs decreased at higher dispersal values: smaller MPAs performed worse than large MPAs. Figure 3.6 Figure 6 Biomass change in different MPA configurations. The bars show the biomass density of large, medium and small reef fish relative to the smallest biomass density value among the 8 MPA size configuration scenarios. The various MPA configurations are shown on the horizontal axis. The MPA sizes associated with the MPA number are as follows: 3.33*30, 5*20, 10*10, 12.5*8, 16.67*6, 25*4, 50*2 and 100 km2*1. Black bars represent large reef fish, white bars represent medium reef fish, and grey bars represent small reef fish. 69 3.4 Discussion 3.4.1 Ecosystem effects of restricting fisheries inside the MPAs The first research question is discussed under the following three sub-headings: dispersal rate, no-take areas and trophic cascade. 184.108.40.206 Dispersal rate The functional groups in the model with low dispersal rates responded most to protection from MPAs. Dispersal rate is the parameter to which biomass distribution in Ecospace model is highly sensitive. Others have made similar observations (Watson et al. 2000; Beattie et al. 2002; Piroddi 2008; Christensen et al. 2009). The exchange rate across MPA boundaries is recognized as an important characteristic according to both empirical (McClanahan and Mangi 2000) and other modeling studies (Le Quesne and Codling 2009; Little et al. 2009) in determining the success of the MPA. Species specific or functional group specific dispersal rates are not very well known. The uncertainty in the dispersal rates used in the Ecospace model therefore has huge implications on the application of model results to the real world. Incorporating a sensitivity analysis on the dispersal rates in Ecospace will lead to a better understanding of the implications of the uncertainty on the results and monitoring existing and experimental closures will increase understanding of actual dispersal rates (Christensen et al. 2009). However, as suggested by Pitcher et al. (2002) ―precise results, but not overall patterns are sensitive to uncertainties‖. It is clear that for more mobile organisms, the optimum size for closed area increases. There is thus no ‗one‘ optimum size for an MPA; the decision on size depends on the major species for which the protection is aimed at. New approaches designed to protect far ranging pelagic species include protecting ―demographically critical areas‖ where the populations have higher vulnerability (Game et al. 2009) or ―temporary spatial closures‖ with the location of the closed areas changing during the course of the year (Grantham et al. 2008). 70 220.127.116.11 No-take areas and spillover Another clear result from the Ecospace analysis was that a no-take area of ‗some‘ size was needed for rebuilding the population. Compared to partial fishing restrictions, the increases in biomass density observed when the MPAs were set as no-take were much higher. A similar result was obtained in an analysis of dolphin populations in Ionian Sea– –when no fishing was allowed—rebuilding of dolphin populations occurred, but the dolphin populations showed only a small increase when the artisanal fisheries were allowed (Piroddi 2008). Among the three spillover regions compared, the relative increase in fishing effort in the spillover region was highest in Kofiau. It has been suggested that higher fishing effort in the spillover region encourages spillover (Walters et al. 2009). Studies also show that high spillover across a long perimeter of an MPA can drain the MPA of the rebuilding fish biomass (Watson et al. 2000). The results also indicated a trade-off between catch in the spillover region and catch inside MPA under restricted fishing effort scenarios. The results have implications on MPA design—whether a buffer zone should be placed between the closed (no-take) and open areas. Spillover would depend on the type of fisheries allowed in the buffer zone and the trophic cascade effects. If the buffer zone fisheries are for example artisanal hook and line fisheries, then they might target only the top predator species in the buffer zone and not dilute the spillover of other reef fish and pelagic fish for the drift-net fishers outside the buffer zone. More modeling effort is needed to understand if buffer zones would enhance or dilute the spillover effects. It might be possible to design MPA zoning in concordance with the dispersal rate of species with very selective gears allowed in the respective buffer zones. Spillover from a reserve would also depend on distance from the reserve (Russ et al. 2003), on non-fisheries aspects like tidal flow and reef morphology (McClanahan and Mangi 2000), and on whether fishers enter the spillover habitat area and find it suitable to fish (Forcada et al. 2009). 71 18.104.22.168 Trophic cascade The increase in biomass of large reef fish inside the MPA depressed the population of medium reef fish leading to an increase in biomass of small reef fish. The trophic cascade in MPAs has also been reported in other studies using Ecospace (Piroddi 2008) with high predator densities and low prey densities inside the MPA. A comparison of unfished reef versus fished reefs has shown that a larger population of higher trophic level species ―overwhelmed the fish assemblages so that the biomass pyramid was inverted‖ (Sandin et al. 2008). Trophic cascade could be a reason why population increase of mid-trophic level species in an MPA may moderate. Mid-trophic level species will respond to protection when the release from fishing pressure is greater than the increase in predation pressure under MPA protection. However, changes in size structure of mid-trophic level species due to reduced fishing pressure in an MPA could offset the ―negative impacts of enhanced predation‖ (Mumby et al. 2006). 3.4.2 Ecological benefits of single large versus several small MPAs Research using Ecospace models have favored larger MPAs (Martell et al. 2005). Large MPAs would be needed to offset high exchange rates of the fish especially in situations of food limitation, excessive predation pressure and shifting of productive areas due to changes in ocean circulation patterns (Martell et al. 2005); this may be an argument for large MPAs in areas with ephemeral upwelling regimes. Larger MPAs enhanced spillover owing to ―spatial cascade effects‖—high predator and low prey biomass inside MPA and vice-versa outside—and fishing effort concentration outside the MPAs (Piroddi 2008). In ecosystem models, larger protected areas were able to restore fisheries while smaller protected areas were unable to avert collapses in a highly exploited ecosystem in the South China Sea (Pitcher and Buchary 2002). Small sizes of MPAs and movement of fish into the spillover regions could render the MPA ineffective (Walters 2000). Spatially explicit population dynamic modeling arrived at similar conclusions: Stefansson and Rosenberg (2005) concluded that large percentages of fish biomasses needed to be protected for rebuilding a stock and small area closures were ―unlikely to give substantial protection‖. 72 Following the work on the Raja Ampat Ecospace model, which showed that some amount of no-take area is essential, the next step was to determine the ―ideal minimum size‖ of no-take zones inside a reserve. The results for Kofiau and Misool indicate that after an increase in the size of no-take areas beyond 16 to 25 km2, the benefits calculated in terms of biomass density of reef fish asymptote. This result was opposite in the Dampier Strait model, but some dynamic instability was present in that model casting doubt on the finding. Based on the Kofiau and Misool Ecospace model results, the ideal minimum size of a no-take area in Raja Ampat is 16 to 25 km2; however, the precise values cannot be relied upon as management advice. Since the analysis is done for reef fish that are (in general) less mobile, the estimate of minimum size of ―no-take‖ area is conservative; the value would only be higher for more vagile species. Other uncertainties associated with model parameterization also influence the results. Factors not addressed in the Ecospace model such as habitat quality improvements (Jiang et al. 2008), hydrodynamics, spawning aggregations and source and sink populations will also influence the ideal minimum size of no-take area. However, biomass density benefits from MPAs asymptote as reserve size increases. The research on the minimum size of no-take areas offers options to integrate the ecological result with social considerations that might favor smaller or larger no-take areas. For Waigeo Island, some plans exist for small 0.2 km2 no-take zones, no zoning plans have been made for Kofiau and Misool (Rahwarin B. pers. comm.).19 In an area like Misool, that has a diverse human population; it might be difficult to implement a single large MPA owing to a wide array of customary tenure agreements and/or difficulty in arriving at a consensus because of large number of players. In terms of population, Kofiau is relatively homogenous, and Boo island (part of Kofiau see Figure 3.1) is relatively uninhabited; thus, it might be feasible to declare a large no-take area around Boo Island. These opinions are based on cursory understanding of the social dynamics. Clever zoning that will result in successful protection will need community and administrative collaboration, probably encouraged by the presence of NGOs. 19 Becky Rahawarin. DKP, Raja Ampat. Jl. A. Yani, Kuda laut, Sorong, Papua. 73 3.5 Conclusion Though there is scepticism (Willis et al. 2003) about the utility of MPAs as fisheries management or conservation tools, several empirical and modeling studies have demonstrated the biomass and spillover enhancing potential of reserves (Russ et al. 2003; Jiang et al. 2008; Little et al. 2009; Stelzenmuller et al. 2009). To improve management potential, Sale et al. (2005) identified gaps in the research (distance and direction of larval dispersal, movement patterns in juveniles and adults, changes to community structure due to trophic cascades, hydrodynamic patterns) that complicate the decision regarding size and placement of MPAs. This chapter based on Ecospace modeling has tried to address issues about dispersal, trophic cascades, and the size of MPAs. Buffer zones are an interesting research direction for future work, with possibility for phased (in concordance with dispersal rates of species) deployment of very selective gears in successive buffer areas. Near Apo Island, Philippines, the purpose of an established reserve was to ban nonresidents from the fishing ground and prohibit destructive fishing gears; their management goals were similar to those in Raja Ampat. It is interesting that success of the MPA near Apo Island later led to increased revenues from tourism, thus the ―islanders had to fish less to support their families‖ (Russ et al. 2004). Success of MPAs will depend on ―understanding of the spatial structure of impacted fisheries, ecosystems and human communities‖ and ―careful planning, evaluation and appropriate monitoring programs‖ as stated in Hilborn et al. (2004) and echoed 5 years later by Le Quesne (2009). 74 3.6 References Ainsworth C. H., D. A. Varkey, and T. J. Pitcher. 2008a. 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Buchary, E. and P. Trujillo, editors. Spatial Simulations of Hong Kong's Marine Ecosystem: Forecasting with MPAs and Human-Made Reefs. Fisheries Centre Research Reports 10(3):27-35. Pitcher T. J., E. A. Buchary, and T. Hutton. 2002. Forecasting the benefits of no-take human-made reefs using spatial ecosystem simulation. ICES Journal of Marine Science 59:17-26. Russ G. R., A. C. Alcala. 2003. Marine reserves: rates and patterns of recovery and decline of predatory fish, 1983-2000. Ecological Applications 13(6):1553-1565. Russ G. R., A. C. Alcala, and A. P. Maypa. 2003. Spillover from marine reserves: the case of Naso vlamingii at Apo Island, the Philippines. Marine Ecology Progress Series 264:15-20. Russ G. R., A. C. Alcala, A. P. Maypa, H. P. Calumpong, and A. T. White. 2004. Marine reserve benefits local fisheries. Ecological Applications 14(2):597-606. 78 Sale P. F., R. K. Cowen, B. S. Danilowicz, G. P. Jones, J. P. Kritzer, K. C. Lindeman, S. Planes, N. V. C. Polunin, G. R. Russ, and Y. J. Sadovy. 2005. Critical science gaps impede use of no-take fishery reserves. Trends in Ecology and Evolution 20(2):74-80. Salomon A. K., N. P. Waller, C. McIlhagga, R. L. Yung, and C. Walters. 2002. Modeling the trophic effects of marine protected area zoning policies: A case study. Aquatic Ecology 36(1):85-95. Sandin S. A., J. E. Smith, E. E. DeMartini, E. A. Dinsdale, S. D. Donner, A. M. Friedlander, T. Konotchick, M. Malay, J. E. Maragos, and D. Obura. 2008. Baselines and degradation of coral reefs in the northern Line Islands. PLoS One 3(2):e1548. Schrope M. 2008. Conservation: providential outcome. Nature 451(7175):122-123. Stefansson, G. and A. A. Rosenberg. 2005. Combining control measures for more effective management of fisheries under uncertainty: quotas, effort limitation and protected areas. Philosophical Transactions of the Royal Society Biological Sciences 360: 133-146. Stelzenmuller V., F. Maynou, and P. Martín. 2009. 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Walters C. J., D. Pauly, and V. Christensen. 1997. Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Reviews in Fish Biology and Fisheries 7:139-172. Watson R., J. Alder, and C. Walters. 2000. A dynamic mass-balance model for marine protected areas. Fish and Fisheries 1(1):94-98. 79 Willis T. J., R. B. Millar, R. C. Babcock, and N. Tolimieri. 2003. Burdens of evidence and the benefits of marine reserves: putting Descartes before des horse? Environmental Conservation 30(02):97-103. 80 4 Reconstructing Ancient New Zealand Snapper Biomass from Archaeological Data20 4.1 Introduction 4.1.1 Fisheries management and restoration Overfishing and the consequent collapse of marine ecosystems have been veiled by the shifting baselines syndrome (Pauly et al. 1998; Jackson et al. 2001; Pitcher 2001) wherein the cognitive baseline of each generation for pristine nature shifts towards a more exploited system21. The Food and Agriculture Organization of the United Nations maintains a global repository of fisheries‘ statistics which includes records from 1950, but this short time period (1950-present) that does not cover the long period of exploitation faced by many species for centuries/millenia prior (Roberts 2007). When fisheries catch data from 1950 (or later) is used as baseline in fisheries assessments, the models erroneously assume that the species were at their unexploited biomass levels at that time; the pre-1950 declines in biomass are ignored. For example, current stock assessments for Gulf of Maine cod are based on a fraction of the ancient biomass (Rosenberg et al. 2005). Other impacts of human fishing activity—decline in the size of rockfish in British Columbia, Canada (McKechnie 2005) and in California (Love et al. 2002; Braje 2009)—have been reported by archaeological studies. When research is based on under-estimates of unexploited population biomass or growth, the calculations could lead to erroneous reference points for fisheries management strategies. 20 A version of this chapter will be submitted for publication. Varkey, D. A., Pitcher, T. J., Leach, F., MacDiarmid, A. Exploring ecosystem carrying capacity – Reconstruction of New Zealand snapper population using archaeological data. 21 Each generation thinks that the state of the ecosystems during its time represents the pristine state of nature, but in reality the ecosystem changed in the period when it was exploited by the previous generations 81 When working towards marine ecosystem-based management, it is valuable to understand the carrying capacity of the ecosystem, and how species assemblages have changed through history. Apart from satisfying our curiosity about ancient fishing, ―understanding the influence of human predation on marine resources‖ (Leach 2006) and the underlying drivers (Campbell et al. 2009) is an important component of rational management (Pitcher and Lam 2010). Potential sources of information on the carrying capacity of a system are estimates of ancient abundance; information on carrying capacity will help guide rebuilding and restoration. 4.1.2 Modern snapper fishing in New Zealand New Zealand Snapper (Chrysophrys auratus), a member of the family Sparidae (sea breams), are present mostly between 10 to 60 m depth; therefore, they are commonly found within a few kilometers of the shoreline. Snapper fishery is an important contributor to the coastal fisheries in New Zealand. Total annual snapper catches from the late 1980s to 2007 have ranged between 6,000 and 8,000 tonnes (MFish New Zealand 2007). When signs of overfishing were observed in mid 1980s, a quota management system (QMS) was introduced (Davies and McKenzie 2001), and QMS continues to be the management regime in the present. The analysis is based on snapper population in SNA 8 (Figure 4.1), one of the five snapper management areas in NZ. Snapper catches from SNA 8 contribute about one-fifth of the total snapper landings. The snapper stock in SNA 8 approximates a biological stock, including mainly the stock which recruits from Kaipara Harbour and some other smaller stocks (Paul22 pers. comm.). For stock assessment purposes, SNA 8 is considered to be ―separate from other snapper stocks and to be defined by the SNA 8 management area‖ (Davies et al. 2006). 22 Larry Paul, National Institute of Water & Atmospheric Research, New Zealand 82 Figure 4.1 Map of North Island of New Zealand. The modern data are from snapper management area SNA 8 and the ancient sample is from Twilight beach which is located at the northern end of the ninety mile beach which is shown as a dark strip in the figure. 4.1.3 Prehistoric fishing in New Zealand Ancestors of today‘s Maori were Polynesian immigrants who arrived in New Zealand about 800 years ago (Wilmshurst et al. 2008). They possessed a long tradition of fishing and maritime skills. Maori fishing was typically confined to coastal waters less than 100 m deep, and their fishing methods included seine nets, small hand nets, set nets, hoop nets, basket like traps, netting walls, and hook and line fishing (Leach 2006). Leach (2006) describes the ease of capturing snapper: ―Snapper have strong spines which become entangled in almost any mesh and are seldom caught by the gills. If there are plenty of snapper to be caught, you would only need a net with very large mesh size.‖ The description of seine nets was catalogued by explorer Joseph Banks as ―being so big (80 to 100 fathoms long and 5 to 6 feet wide) that it takes all the inhabitants of the village working together to pull one (Doubtless Bay, North Island)‖ (Leach 2006). The most abundant fish in Maori catches in New Zealand were: barracouta (Thyrsites atum), blue cod (Parapercis colias), snapper, and spotty (Pseudolabrus celidotus). 83 Snapper remains were found in 54 archaeological sites in New Zealand, most of them on the North Island. Length data were obtained from fish remains (n = 1914) (referred to as ‗ancient snapper‘) at Twilight Beach, the site with the highest (92.6%) relative abundance (compared to other fish) of snapper bones (Leach 2006). The Twilight Beach archaeological site is located in SNA 8 at the northern end of Ninety Mile Beach (Figure 4.1). Middens at this site have been dated to the period 1400 -1500 AD (Leach 2006). It is expected that the middens from Twilight beach represent the ancient fish population in SNA 8. A study of spatial distribution of modern snapper showed that the strength of different year classes for fish 5 years and older were quite consistent throughout SNA 8; and inferred ―little spatial variation in average growth rate of snapper‖ (Walsh et al. 2006). In particular reference to Ninety Mile Beach, they found that the ―spread‖ of length at age was greater for fish older than 9 years (Walsh et al. 2006). The objective was to estimate the ancient biomass (c. 1400AD) of New Zealand snapper combining archaeological data with tools in fisheries science. Candidate growth curves for the ancient snapper were proposed; total mortality estimates for the corresponding growth curves were calculated. The growth and mortality estimates were combined in an equilibrium age structure model to estimate the ancient population biomass. Finally, the estimates of ancient snapper biomass were contrasted with published estimates of modern snapper population biomass from stock assessments and surveys in New Zealand and a benchmark for ancient population size was provided. 4.2 Methods 4.2.1 Growth parameters of the modern population The growth of the modern snapper population can be described by the von Bertalanffy growth function (VBGF): (1) Lage L 1 e k ( t t0 ) 84 where, L∞ is the length at which the growth of fish asymptotes, ‗k‘ is the metabolic growth coefficient, and ‗t0‘ is the initial condition parameter (point in time when the fish has zero length). The modern published estimates of L∞ of snapper population in SNA 8 range from 528 mm to 709 mm (see Table 4.1 for details and sources). Modern agelength data for SNA 8 snapper were available for years 1973 to 2007 from the Ministry of Fisheries, New Zealand. A VBGF was fitted (using ‗vonb‘ function from the UBCFC package in R (Martell 2005) to the modern age length data to obtain another estimate of the growth parameters L∞ and k and to. The ‗vonb‘ function uses a non-linear least square (nls) fitting function (R Development Core Team 2009) to estimate the VBGF parameters. Table 4.1 VBGF parameters for modern snapper populations. Serial number L∞ (in cm) k t0 Reference 1 63.2 0.138 -0.72 (Walsh et al. 2006) 2 70.9 0.113 -0.87 (Walsh et al. 2006) 3 66.3 0.125 -0.8 (Walsh et al. 2006) 4 66.2 0.143 -0.52 (Walsh et al. 2006) 5 65.8 0.13 -0.75 (Walsh et al. 2006) 6 57.4 0.17 -0.56 (Davies et al. 2003) 7 63.5 0.13 -1.07 (Davies et al. 2003) 8 56.2 0.18 -0.3 (Davies and McKenzie 2001) 9 52.8 0.21 -0.28 (Davies and McKenzie 2001) 85 Serial number L∞ (in cm) k t0 Reference 10 66.9 0.16 -0.11 (McKenzie et al. 1992; cited in Davies and McKenzie 2001) 11 58.6 0.1557 -1.016 VBGF fit estimate 4.2.2 Midden descriptions/archaeological data Calculation of body length from midden data is a laborious process (Leach 2006). A series of allometric relationships between morphometric measurements (mainly of various snapper bones relative to snapper fork length) were estimated for a wide size range of modern specimens. More details about calculations and bones used for the allometric relationships can be found in Leach and Boocock (1995). The allometric relationships were used to estimate the corresponding fork lengths for the fish bones found in the middens. Age information corresponding to the length data was not available. In general, it is difficult to obtain age data from archaeological specimens. Otoliths are only rarely extracted from archaeological sites, and when they are, archaeological otoliths are very difficult to read annuli from under thin section (Foss Leach pers. comm.). However, in a study (Helen Neil pers. comm.) few (~10) snapper otoliths were obtained from a c.1400 AD midden at Hot water Beach in Hauraki Gulf and were used for a comparison of ancient and modern growth patterns. Their comparison based on standardized annual increment widths from modern and ancient otoliths did not indicate a difference in growth pattern between the ancient and modern otoliths (Helen Neil pers. comm.). Dr Larry Paul also echoed similar subjective impression based on his experience in otolith interpretation. Lengths corresponding to the ancient otoliths were not available, so it was not possible to gauge any age-length information from the ancient otoliths. 86 4.2.3 Growth parameters of the ancient population All the growth parameter combinations of the modern population were tried as potential growth curves of the ancient population to see if the growth curves of modern population could explain the length frequency distribution in the ancient data. Studies that have explored the issue of prehistoric impact on the New Zealand snapper fishery found no conclusive evidence for a ―decline in mean fish size during the preEuropean period‖ (Leach et al. 1997; Leach and Davidson 2001). They have, however, concluded on definite differences in mean-size of snapper caught in modern versus ancient times. The length frequency distributions based on archaeological samples were very different from those based on modern fish catch (Leach and Davidson 2000). The proportion of large (> 600 mm) fish was much higher in the ancient than in the modern samples (Figure 4.2). Hence, it was necessary to explore if growth curves other than modern published growth curves explained the higher proportion of large fish in the ancient population. Figure 4.2 Comparison of length frequency data for ancient and modern snapper populations. 87 4.2.4 Candidate growth parameters of the ancient population A range of L∞ values from 660 mm to 1046 mm (at increments of 1mm) were chosen as potential candidate L∞ values. The lower end of this range represented the upper quartile of the modern growth parameters while the upper end constituted the highest probable value of L∞ calculated using the empirical formula: (2) L Lmax / 0.95 Where, Lmax is the maximum length of fish in the ancient data. Thus L∞ for the ancient population smaller than the modern published values of L∞ were not explored; this was because larger fish were observed in the ancient population. For each individual L∞ value within this range, a corresponding ‗k‘ parameter was estimated by fitting a VBGF to the modern age length data to optimise for ‗k‘ alone (‗t0‘, was assumed to be -1yr). The L∞ and k parameters thus calculated lie on an isocline (Figure 3a). By fitting to the modern age length data, it was assumed that the growth pattern of the snapper population had not changed between the ancient and modern times. This assumption was in agreement with the observations made by archaeologists who compared ancient and modern snapper otoliths. Thus the possibility of ‗no change in growth pattern‘ was explored. It is possible that the k parameter for the ancient snapper was slightly overestimated because of size selective mortality in the modern snapper population. The implications of such a bias are discussed later in the chapter. Thus 386 candidate growth curves for the ancient population (Figure 4.3) were evaluated in the analysis. 88 Figure 4.3 Candidate growth curves for ancient population. 3a. Modern L∞-k combination in filled black dots and candidate L∞-k combinations for ancient population in grey dots. 3b. Open black dots show modern age-length data. Black lines show modern published growth curves and the grey curves show the growth curves based on candidate growth parameters for the ancient population. 4.2.5 Estimation of mortality for ancient population The length-frequency data of the fish remains found at the archaeological middens represent a mixture of several (unknown) age groups in the population. In order to fit these mixture distributions to length frequency data, a method developed by Macdonald and Pitcher (1979), updated by Macdonald and Green (1988), and rewritten for R (‗mixdist‘ package) by Macdonald and Du (2004) was applied. The package applies a standard maximum likelihood estimation (MLE) method to calculate the mixing proportions (the proportion of each component age class in the total population) and the mean and standard deviations of each component distribution (Du 2002), thereby minimising the difference between the sum of the component distributions and the data length frequency. The R program requires a complete set of initial parameter values (i.e., the mean and standard deviation for each length at age, and the proportion of each age cohort in the population). 89 The initial parameters were set up as follows: i. For each L∞-k combination, the mean length at age was calculated using the VBGF equation. A constant coefficient of variation of 10% was applied to the mean length at age to calculate the standard deviations for mean length at age. ii. The proportions of the age-bins were set to be equal (i.e. similar number of individuals in each age-bin). Initializing the proportions using any (other) survivorship schedule could amount to providing prior information about the total mortality in the ancient population, so the proportions were initialized at same value for all age-bins. A set of survivorship schedules were tested as alternate values to initialize the proportions to test if the results were sensitive to the starting values. The influence of different initial values for the proportions is discussed later in the chapter. iii. During the fitting process, the mean lengths at age were fixed. Thus, the fit was achieved by changing the proportions of each age group in the population. An example of a fit is shown in Figure 4.4a. 90 Figure 4.4 Estimation of total mortality (Z) 4a. An example of fit to mixture distribution. The blue bars are the histogram of the lengths from archaeological data, the green line is the cumulative length frequency distribution, and the red lines are the component distributions for each age. 4b. An example of the graph of log of proportions against age (L∞=850mm and k = 0.084). Total mortality Z (0.26) was calculated as the negative slope of regression. The total mortality (Z) corresponding to each L∞-k combination was estimated as the negative slope of the fitted proportions. Two problems with length frequency analysis are that sometimes knowledge about number of age-classes is not available, and that several combinations of parameters could fit the length frequency distribution (Schnute and Fournier 1980). This author experimented with several combinations of number of agebins. At higher ages (>35), the log proportions did not decline in a straight line but became a curve (i.e. the proportions of older fish were being overestimated); so ages>35 were not used as age-bins. In the analysis presented here 15 age classes were used (alternate age from age 3 to 31) in the fitting process. Fewer age bins (<15) were not used because grouping ages together could lead to loss of information in the length frequency. The goal was to obtain an estimate of Z corresponding to each L∞-k combination. 91 4.2.6 Proportion of large fish in the population The proportion of fish of different lengths in a population depends on the growth curve of the species and the survivorship at age (the probability fish surviving to each age). The proportions of fish above three length (600 mm, 750 mm, and 800 mm) values in the population were calculated using both the modern published and the candidate growth curves. First the proportion of fish above the three length levels in each age class was calculated based on length at age and standard deviation of length at age (for example P800_20 represents fish at age 20 which are greater than 800 mm). The sum-product of survivorship schedule and the proportion of fish above the three length levels in each age class gave the proportion of fish above the three length levels in the population. (3) Survivorship (lage) is the probability of surviving to each age and is calculated from estimates of total mortality as follows: lage exp( Z ( age1) ) . For each candidate growth curve, the Z estimated in the earlier section was used to calculate the survivorship. (4) Proportion of fish greater than 800 mm: P800 P800_ age.lage It was assumed that after the fish fully recruited to the fishery, the proportion of fish of different lengths in the data represented their ratios in the fully recruited part of the population. In the estimation of total mortality it was found that fish above 600 mm had fully recruited to the fishery23. The proportions of fish greater than lengths 750 mm and 800 mm in the fully recruited part of the population were estimated. For example, the proportion of fish greater than 800 mm in the fully recruited part of the population Pfr_800 was calculated as: (5) Pfr _ 800 P800 / P600 23 Figure 4.4b shows that fish above age 9 have fully recruited to the fishery, the log of proportions at age starts descending. Figure 4.3b shows that age 9 fish correspond to length around 500 mm, thus it is judged that fish above 600 mm should be fully recruited to the fishery. However, this author is not assuming that 600 mm is the smallest length at which the fish fully recruit to the fishery. 92 The estimated proportions of large fish (fully recruited) were compared with their respective proportions in the ancient data. The comparisons that fell within the proportion of large fish observed in the ancient data + 10% were chosen as the probable L∞-k combination for the ancient population. By choosing the estimated Z to model mortality at all ages in the population this author made an assumption that the total mortality remained constant throughout all life stages. The results of P600, P700, and P800 were highly sensitive to different formulations of Z (i.e. combination of natural mortality (M) and fishing mortality (F) at smaller ages. But the results of Pfr_600, Pfr_700, and Pfr_800 were not sensitive to the assumptions on Z (for the time before the fish fully recruited to the fishery) because after the fish fully recruited to the population, the decline in proportions was dependent on the estimated Z. 4.2.7 Ancient population size An equilibrium age-structure model was used to estimate the ancient population size. The product of biomass per recruit and equilibrium number of recruits gives the equilibrium size of the population. Note that emphasis should not be placed on the precision or accuracy of the resulting estimates. Rather the intent was to provide approximate estimates of ancient population size in order to facilitate a discussion the difference between the ancient and the modern population biomass. 22.214.171.124 Biomass per recruit The biomass per recruit (B/R) is calculated as the sum-product of weight at age and survivorship at age. Paul (1992) found the maximum age of a snapper to equal 60 years and this estimate was used for the analysis. The estimates of Z from the earlier section were used to model survivorship. I only had estimates of Z for the ancient population; I did not have any estimates of natural mortality (M) or fishing mortality (F). To split the estimated Z into its components and to estimate the probable ranges of B/R in the ancient population, it was assumed that M for the ancient population could range between the following two levels: 93 i. M=0.057 (F=Z-M) (M was equal to the natural mortality on the modern population (Davies et al. 2006; MFish New Zealand 2007), ii. M=0.114 (F=Z-M) (M was double the natural mortality on modern population.) Overestimating survivorship would lead to a higher estimate of biomass per recruit and therefore higher population biomass; underestimating survivorship would lead to a lower estimate of biomass per recruit and lower population biomass. In the estimation of total mortality in the earlier section, it was observed that the fish became fully vulnerable to fishing at around age 9 for all the candidate growth curves tested (see example for one growth curve in Figure 4.4b). The vulnerability at age of the ancient snapper population was modelled using a logistic curve (Figure 4.5); the length at which 50% fish were vulnerable (l50) was 450mm. (6) Vulnerability to fishing at age: vulage 1 (l age l50 ) 1 exp sig where, sig represents the steepness of the curve Figure 4.5 Vulnerability at age to fishing 94 (7) Fishing mortality at age: Fage F .vulage (8) Survivorship at age: At age =1 l f 1 At age> 1 l f l f ( age1) exp( M F( age1) ) (9) Weight (Wage) at age according to L-W relationship: Wage a(lage ) b Where, a was set to 0.0447 and b to 2.793 based on information in Fishbase (Paul 1976; as recorded in Freose and Pauly 2010) and lage was calculated according to eq 1. The B/R was calculated as the sum-product of the weight at age and the survivorship schedule. (10) Biomass per Recruit: B / R Wage.lage 126.96.36.199 Stock and Recruitment It was assumed that both the modern and the ancient population follow the same stock recruitment curve. Recruitment was described using the classic Beverton and Holt stock recruitment (BH-SR) pattern (Myers 2001): (11) R S (1 S ) where, R represents recruitment to age 1, and S breeding stock size. Parameter α is the slope at the origin, ―α increases the height of the asymptote and reduces the curvature, and β increases the rate of approach to the asymptote‖ (Jennings et al. 2001). The ‗α‘ was 95 set to 1.287 based on Myers et al. (1999)24. The parameter β was calculated using the relation: (12) R0 R0 was obtained from the plot of species summary for the New Zealand snapper from SNA 8 by Myers et al. (1995). According to Beverton and Holt SR pattern, with an increase in number of spawners, the number of recruits increases to an asymptote. The equilibrium mean recruitment at a given level of exploitation is calculated by the formula (refer Walters and Martell (2004) for more details): (13) Re (e 1) e where, Re is the mean recruitment, and e is the fecundity incidence function, which represents the fecundity per recruit in the population and is calculated from maturity at age and fecundity at age as follows: (14) Maturity at age mat age 1 ( age age mat ) 1 exp sig (15) Fecundity at age fec age wage.matage (16) Fecundity per Recruit e fec age.l age 24 the ‗α‘ value is estimated as â/SPRF=0. (Myers et al. 1999) used SPRF=0 = 50.956 (from Annala and Sullivan 1996) and estimated the â as 65.6. Annala, J.H. & Sullivan, K.J. (Comps) 1996: Report from the Fishery Assessment Plenary, April-May 1996: stock assessments and yield estimates. 308 p. (Unpublished report held in NIWA library, Welington). Enquiries: N.M. Davies, NIWA, P.O. Box 1043, Whangarei, New Zealand. Email: email@example.com 96 It was assumed that above the age at maturity, the fecundity was proportional to body weight. Age at maturity was set at 3 years based on observations that snapper in SNA 8 mature at age 3 (Davies et al. 2006). The population biomass was calculated as the product of B/R and mean recruitment (17) Biomass ( B / R) * Re 4.3 Results 4.3.1 Growth parameters of the ancient population Corresponding to higher candidate L∞ values for the ancient population, higher estimates of total mortality Z were obtained. Thus estimates of mortality were correlated with L∞. The modern published growth curves were not able to explain the proportion of large fish (>750 mm and 800 mm) in the recruited fish population (Figure 4.6a and 4.6b). The proportion of fish greater than 750 mm was explained by candidate growth curves with L∞ in the range from 767 to 853 mm; L∞ higher than 853 mm overestimated the proportion of fish larger than 750 mm. The proportion of fish greater than 800 mm was explained by candidate growth curves with L∞ in the range from 826 to 900 mm. When the uncertainty around the estimate of Z was considered, the range increased and included fish from 735 mm to 1050 mm. For the fitting process used to estimate the Z, the initial values for the proportions at age were set to be equal. When different initial values for the proportions at age were tested25 25 The different starting values were survivorship schedules corresponding to Z within the range 0.14 to 0.34. When the starting values were changed, different estimates of fitted proportions were obtained. For candidate growth curves in the range L∞ 660 mm to 827 mm, the best fits were obtained when the initial proportions for all age classes were set to be equal. For candidate growth curves with L∞ values 828 mm and above, better fits were obtained when different survivorship schedules were used as initializing values. These better fits for candidate growth curves with L∞ values 828 mm and above resulted in slightly different (~1%) estimates of Z. 97 in the fitting process slightly different results were obtained; the proportion of fish greater than 750 mm were explained by candidate growth curves with L∞ in the range from 767 to 841 mm; the proportion of fish greater than 800 mm were explained by candidate growth curves with L∞ in the range from 826 to 884 mm. Since the difference in results was not large, the results obtained based on the earlier initialization of proportions (all age classes set to be equal) were used to calculate the estimates of biomass presented in the following sections. Figure 4.6 Proportions of large fish (>750 mm and 800 mm) estimated using modern published and candidate ancient growth curves. The dark grey dots show the proportions estimated by the total mortality estimates from fitting; the light grey dots are obtained using the upper and lower bounds of the total mortality estimates. The black dots are estimates using modern published growth curves. The solid horizontal line is the proportion of fish above the corresponding length in the ancient data, and the dashed lines are proportions 10% above and below the solid line. 4.3.2 Comparison of modern and ancient population biomass Current estimates for mean of snapper biomass calculated based on several stock assessment models for SNA 8 range from 11,200 to 12,900 tonnes with confidence limits ranging from 9,600 to 16,500 tonnes (Davies et al. 2006). Trawl survey estimates for year 98 2002 put the value at 10,442 tonnes with a coefficient of variation of 0.12 (Davies et al. 2006). The range of the ancient population size depended on the growth parameters combinations. A larger L∞- smaller k combination yielded a lower ancient population biomass estimate compared to a smaller L∞- larger k combination (Figure 4.7). Results showed that the ancient population size (20,000 to 38,000 tonnes) was about 2 to 4 times higher than the modern population. Very little change (factor of 1.01) in the equilibrium recruitment was observed, and the factor by which the ancient population was higher than the modern population depended mainly on the ratio of biomass per recruit in ancient versus modern times. Figure 4.7 Ancient snapper population biomass. The biomass estimates are plotted against the candidate growth parameters for the ancient population. The solid line shows the estimates corresponding to estimated Z (corresponding to dark grey dots in Figures 4.6a and 4.6b). The dashed lines show the estimates including the lower and upper bound on the estimates of Z (corresponding to light grey dots in Figures 4.6a and 4.6b). 99 4.4 Discussion Steady state conditions were assumed in the analysis. The period from 1500 AD – 1770 AD is known as the classic Maori culture at which time the Maori population was at its peak. The midden data belonging to 1400 AD – 1500 AD were from a time period when the Maori population and their use of marine resources (including snapper) was changing. The snapper fishery was commercially exploited since the mid-1800s, with catches highest in the period 1960 to 1980 (Maunder and Starr 2001). The modern data belong to the period 1973 to 2007 AD, also a period which saw change in the exploitation pattern. Hence, in the short term perspective, population levels for both time periods were not in steady state. However, from a long term perspective and for the comparison of populations 600 years apart in time, the snapper populations could be assumed to be in steady state. 4.4.1 Ancient growth parameters L∞ was estimated to range between 767 mm and 900 mm, while modern published values range from 528 mm to 709 mm. The lower estimate (767 mm) is only 6 cm higher L∞ than the highest modern published estimate for L∞. The question is whether the difference between ancient and modern L∞ is the result of evolutionary change or an artefact of the VBGF. If the fishing pressure on a population is high, then the proportion of large individuals in the population would be expected to be low (i.e., modern day scenario). ―Extirpation of large specimens by intensive fishing‖ has made it difficult to estimate the maximum size of fish species (Binohlan and Froese 2009). Fitting a VBGF curve to agelength data from such a fishery catch can bias the L∞ downward and the growth parameter k upward. The lower modern L∞ could, therefore, be an artefact of fitting to data largely containing small age groups and rare large fish. The largest fish seen in the modern age length sample was 830 mm, but the fraction of such large fish was very small. So it is possible that the lower published estimates of L∞ are a result of rarity of large individuals. Also growth curves of snapper followed a very similar pattern over the first 8 years of life and appeared almost identical in both the modern published and the candidate growth curves for the ancient population (Figure 4.2b). Therefore, it is possible that the L∞ of modern snapper are higher than previously published estimates. 100 This author was thus unable to explore if the higher k seen in the modern estimations is the result of non-availability of large fish in the sample or the actual increase in the growth rate of the snapper. The more important question though is whether this barely discernable difference in growth parameters is of any significance for real management purposes. Answering the question would require analysis of response of population biomass at different combinations of growth parameters. Higher mortality risk has been shown to be associated with faster growing species (Lankford et al. 2001); however, these analyses refer to comparisons of different species or across same species at different latitudes and not to changes in growth in the same fish stocks. Some preliminary analysis indicates that if every other parameter remains the same, a higher k value would lead to a higher estimate of population biomass; a higher k would also underestimate the depletion of the population from the unfished level. But the parameter k is usually correlated with L∞; thus, a sweeping generalization that a higher k could over-estimate the stock status cannot be made. An in-depth analysis is required in this area before any conclusions can be made. As mentioned earlier, otoliths are difficult to extract from archaeological sites and it is also difficult to read annuli from archaeological otoliths. No corresponding age information was available with the length data for the ancient sample. If after extraction from middens, otoliths were weighed before sectioning then estimates of corresponding lengths (with limited confidence) could be made based on the otolith weight. Availability of some length at age would help validate the results of a study like ours. The authors, therefore, suggest that midden otoliths be weighed before being sectioned for further study, so that the length or weight of the fish (to which to otolith belonged) could be predicted. 4.4.2 Ancient population biomass The ancient population biomass was estimated to be about 2 to 4 times higher than the modern snapper population biomass. The contribution to the difference in biomass was mainly from difference in biomass per recruit. The ratio between the equilibrium recruitment levels was close to unity. A relationship between temperature and recruitment 101 in New Zealand snapper has been described (Paul 1976; Maunder and Starr 1988; Francis 1993; Maunder and Starr 2001). The dependence on temperature is an indication that recruitment was not dependent on stock size (Annala and Sullivan 1997; cited in Maunder and Starr 2001), indicating that spawning stocks have not fallen to levels at which the impact of a decline in spawners corresponds to lower levels of recruitment. Annala (1994) as cited in Leach (2006) estimated the B0 (virgin biomass or unfished biomass) of snapper in SNA 8 to be equal to 73,200 tonnes. A number of stock assessment models estimated the mean B0 for SNA 8 for snapper to range between 117,000 and 135,000 tonnes (Davies et al. 2006) with lower and upper confidence intervals in the range from 113,000 to 142,000 tonnes. The stock assessment models assumed that in 1931 the population was in ―unexploited equilibrium‖. The results for unfished biomass in the chapter were more uncertain (50,000 to 150,000 tonnes for L∞ 767 mm and 50,000 to 190,000 tonnes for L∞ 900 mm) depending on the assumption of natural mortality in the analysis. The lower values were obtained when natural mortality was twice the natural mortality on the modern population, and the higher values were obtained when natural mortality on the ancient population was the same as the natural mortality on the current population. The most important parameter influencing the results of population biomass was the natural mortality on the ancient population. Therefore, when trying to estimate the carrying capacity of a population in an ecosystem, it is necessary to understand the natural mortality experienced by the population. The ancient system probably had higher abundance of predators of snapper, therefore, a higher natural mortality than the modern snapper. Under this circumstance, if the fishing mortality on the modern population is removed, the population could probably rebuild to levels higher than the levels observed in the history of the population. The unfished biomass ‗B0‘ is relative to the natural mortality. If the natural mortality has not changed with time, only then the B0 would be equal to the population size at which the population was before the fishery began. However, when several predator species are exploited, the natural mortality constantly changes resulting in a change in the carrying capacity of the species in the ecosystem. 102 4.5 Conclusion The results based on the proportion of large fish show that the L∞ of the ancient population (767 mm to 900 mm) was higher than the published values of L∞ for the modern population (528 mm to 709 mm). When estimating growth parameters, caution should be exercised against over-estimating k and under-estimating L∞. The ancient snapper population in Maori times was 2 to 4 times larger than the modern snapper population. The estimates of unfished biomass (5 to 20 times larger) were highly influenced by the assumption on natural mortality on the ancient population and the uncertainty on the growth parameters in the analysis. If the natural mortality on the modern population is lower than the levels on the ancient population, then the modern population might rebuild to higher levels than the ancient population. The carrying capacity of the current ecosystem could also have changed due to factors such as loss of habitat, pollution, change in biomass of prey species, and competition. When trying to base rebuilding targets on unexploited levels of stock, the changes that have happened in the ecosystem should be evaluated in addition to evaluating the effect of removal of fishing pressure from the species. 103 4.6 References Annala J. H. 1994. Draft reports from the Fishery Assessment Working Groups. February-March 1994. Unpublished report, MAF Fisheries. Annala J. H., K. J. Sullivan. 1997. Report from the Fishery Assessment Plenary, May 1997: stock assessments and yield estimates. Unpublished report held in NIWA library, Wellington. 409p. Binohlan C., R. Froese. 2009. 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Proceedings of the National Academy of Sciences 105(22):7676-7680. 107 5 Evaluation of Restoration Goals for Raja Ampat Coral Reef Ecosystem Using Influence Diagram Modeling26 5.1 Introduction 5.1.1 Need for marine restoration Overfishing has led to declines of fish populations (Myers et al. 1996; Rose and Kulka 1999; Morris et al. 2000; Dulvy et al. 2003; Hutchings and Reynolds 2004) and has pushed several marine ecosystems towards collapse (Hughes 1994; Pauly et al. 1998; Jackson et al. 2001; Pandolfi et al. 2003). With the increase in marine resource declines, the emphasis on restoration has increased (Pitcher 2001; Fox et al. 2003; Russ and Alcala 2003; Lotze et al. 2006). Restoration efforts usually involve modifications to fishing gear, season length, quota allocation, species restrictions, or establishment of marine protected areas. The process of restoration is not easy because all manifestations of restoration require current extractions from the ecosystem to be limited, suspended or stopped. Lack of understanding between the fishers and the management agencies have often led to ―adversarial relations‖ (Kaplan and McCay 2004) rendering any positive step towards restoration difficult (Charles 2002). Studies have highlighted that achieving ―health and stewardship of coastal and marine seas‖ requires incorporating the concerns of different stakeholders (Leslie and McLeod 2007). Pairing marine tourism with fisheries restoration could offer options to protect both the ecosystem and the associated livelihoods 26 A version of this chapter will be submitted for publication. Varkey, D. A., Pitcher, T. J., McAllister, M., Sumaila, R. Restoration strategies for coral reef ecosystems – combining fisheries, tourism and conservation utilities using a Bayesian influence diagram model. 108 (Brunnschweiler 2009). In a comparison of stakeholders priorities in a Caribbean marine park, it was seen that all the stakeholders, from village council, fishers, recreational users and members of the assembly, weighted ecosystem health higher than economic and social concerns (Brown et al. 2001) but another comparison of perceptions in Florida Keys showed that these different groups often had different approaches to the use of the resource (Suman et al. 1999). Therefore, restoration efforts need to focus on management policies, which can incorporate the differing perspectives and attitudes of multiple stakeholders. 5.1.2 Raja Ampat coral reef ecosystem Raja Ampat is an archipelago located inside Southeast Asian coral triangle, a hotspot of marine biodiversity and an area known for the high diversity and abundance of coral reef and fish species (McKenna et al. 2002; Donnelly et al. 2003). About 24,000 fishers depend on the reef and the adjacent coastal waters for their livelihood (Dohar and Anggraeni 2007). Analysis of fisher perceptions (Ainsworth et al. 2008a) and previous ecosystem modeling (Ainsworth et al. 2007) of the coral reef ecosystem shows that many fish populations in the region have declined due to high fishing pressure. Several nongovernmental organisations (NGOs) working in the area publish pamphlets in the local language ‗Bahasa‘ (Rabu 2006) containing information for general awareness of the fishers. Involvement with the NGOs has led to increased understanding of the harmful effects of destructive fishing. So, the fishers also are interested in protecting the ecosystem. As a political entity, Raja Ampat is a relatively new regency (the administrative hierarchy of a regency is one level below the province and roughly corresponds to a district) in the province of Papua in eastern Indonesia. The region has also been declared a ‗Kabupaten Bahari‘ (maritime regency) (Conservation International 2008) to safeguard the marine resource and to encourage tourism to the region. These new developments have led to increased interest in fisheries restoration in the region. The process for co-managing fisheries and tourism in Raja Ampat is based on management guidelines designed for and observed to be successful in Bunaken National Park located in Sulawesi Islands of 109 Indonesia (Erdmann et al. 2004). The Bunaken National Park was established in 1991 and initially it was only a ―paper park‖; after many years of mismanagement, which also saw increases in destructive fishing and declines in fish populations, an independent park management unit was established in 1997 (Erdmann et al. 2004). In a span of 5 years, in an iterative process of cooperation and consultation between the operators in the tourism industry, the local government, villagers, and park management body, a consensus was achieved on the management methods for the park. For in-depth understanding of the process, which brought all stakeholders on board, interested readers are encouraged to refer to the report by Erdmann et al. (2004). 5.1.3 Combining ecosystem model and Bayesian belief network The restoration strategy for the coral reef ecosystem needs to balance the complex interspecies relations, expectations of the fishers, the needs of tourism industries, and the needs of conservationists. Instead of searching for the best policy, the more practical approach is probably to search for the most robust policy: this is the policy that will be suitable under the main sources of uncertainty and differences in utility functions between different interest groups. In this chapter, effort is made to identify restoration strategies for the Raja Ampat coral reef ecosystem that can be robust under different ecosystem states, different levels of tourism development, and different levels of interest in conservation. Bayesian influence diagrams, a special application of Bayesian Belief Networks (BBN) (Jensen 1997; Howard and Matheson 2005), are used to combine utilities of the different stakeholder groups dependent on Raja Ampat coral reef ecosystem. Influence diagrams usually have one or more decision variables, which are informed by a combination of ―knowledge‖ and ―action‖ variables (Kuikka et al. 1999). Each variable in the BBN is associated with a set of probability tables for different states or values of the variable. The states of the ecosystem and fisheries catches are modeled using an Ecopath with Ecosim ecosystem simulation model for the Raja Ampat coral reef ecosystem. Other authors have used influence diagrams to model decision making in fisheries, for example, to decide on a robust management strategy when faced with 110 environment driven uncertainty in cod recruitment (Kuikka et al. 1999); or to combine biological, social, and operational objectives for managing a herring fishery in the Bay of Fundy (Lane and Stephenson 1998). BBN have been used to decide on best allocation of resources available for management (Mantyniemi et al. 2009) and to compare different policies with respect to fisher commitment (Haapasaari et al. 2007). 5.2 Methods 5.2.1 Ecopath with Ecosim The ecosystem simulation model was built using Ecopath with Ecosim (EwE) software. EwE is a mass balance food web simulation model that acts as a thermodynamic accounting system for marine ecosystems. Ecopath is a static snapshot of the system (Christensen 1992) that maps the energy flows in the system while Ecosim allows modeling of species composition changes as fishing effort varies over time (Walters et al. 1997). Marine flora and fauna are aggregated into functional groups based on similarity in their life history and trophic behaviour. The flows between groups are a result of predator-prey (predation mortality) interactions among functional groups and fishing mortality on the species. The EwE model (Ainsworth et al. 2008b presented in Appendix F) is used to explore various fishing restriction scenarios for the restoration of the system; the results in biomass, catch, and revenue are used to inform the probability tables needed for the bayesian influence diagram. However, readers interested in greater detail on the EwE models are referred to the online technical reports (Ainsworth et al. 2007) and (Ainsworth et al. 2008c) (see Appendix F). 5.2.2 Model structure of the influence diagram The influence diagram is presented in Figure 5.1 and details of its component nodes (variables) are presented in the paragraphs below. 111 Figure 5.1 Structure of influence diagram. The rectangular box shows the decision variable (restoration scenarios). The ellipses show all the other variables, unconditioned and conditioned that lead to the calculation of the utility. The different decisions (restoration scenarios) are compared based on the final utility values obtained. 188.8.131.52 Ecosystem restoration scenarios The decision node ‗Ecosystem restoration scenarios‘ consists of a group of fishing restriction scenarios. The restoration scenarios range from specific gear restrictions on the reef to marine protected areas (MPAs), which includes restricting (i) destructive fishing; (ii) fishing for live reef fish; (iii) net fisheries on the reef; (iv) shark fishery on the reef; and finally, imposing three levels of fishing closure: 25% of the model area; 50% of the model area; and 75% of the model area closed. Closures are simulated by reducing the effort 25%, 50% and 75%, respectively, in the Ecosim model27. The Raja Ampat Ecosim model is used to simulate each restoration scenario for 20 years. A ‗status quo‘ scenario with no effort restriction is used for comparison. The ‗status quo‘ scenario is the continuation of the base fishing effort for 20 years and does not depict the ‗status quo‘ open access nature of fisheries management in the region wherein fishing effort could increase over the next 20 years. Since this chapter explores restoration scenarios, scenarios with increasing fishing effort are not explored. A choice of restoration 27 By assuming that 25% reduction in effort corresponds to 25% closure, spatial redistribution of fishing effort that could occur after the MPA is established is ignored; the differences between closing different areas on the map is also ignored. 112 scenarios combined with the starting state of the ecosystem will lead to different levels of ecosystem restoration. 184.108.40.206 Starting and restored ecosystem states These are two nodes in the network, which represent the exploited or restored state of the ecosystem. The ecosystem restoration goals are discretized into four levels, which are four different states of the ecosystem (Figure 5.2)—‗highly exploited‘; ‗medium exploited‘; ‗partially restored‘; and ‗highly restored‘. The node ‗starting ecosystem state‘ is discretized into the first three ecosystem states—‗highly exploited‘, ‗medium exploited‘, and ‗partially restored‘. It is expected that the coral reef ecosystem will be between any of these three discrete levels when the restoration process begins. The node ‗restored ecosystem state‘ can lie anywhere between all the four discrete ecosystem states. The alternate ecosystem states were obtained by running the Ecosim model forward for 20 years at different multiples of the current levels of fishing effort. When restoration is performed, depending on the starting state of the ecosystem and the restoration strategy adopted, the restored ecosystem state will lie somewhere in between the four ecosystem states. The states are described by the relative proportions of coral (hard vs. soft), reef fish biomass (large vs. small and medium), and pelagic fish (large and medium vs. small). A highly depleted ecosystem is low in hard coral, large reef fish, and large pelagic fish; on the contrary, a restored ecosystem has high levels of hard coral, large reef fish, and large pelagic fish. The probabilities for the node ‗restored ecosystem state‘ are calculated using the biomass results, for coral, reef fish and pelagic fish, from the Ecosim model. 113 Figure 5.2 Ecosystem states and restoration goals. The alternate ecosystem states are shown on the horizontal axis. The panels show the composition of reef fish, pelagic fish, and coral in each of the alternate states. The alternate ecosystem states were arrived at by simulating the results of different levels of fishing effort in the Ecosim model. 114 220.127.116.11 Fisheries catch, average price and total fisheries revenue The node ‗fisheries catch‘ represents the total fisheries catch obtained in 20 years over which different levels of fisheries restrictions were in place; it is calculated by summing the total fisheries catch in each simulation year of Ecosim. Depending on restoration scenario and the starting ecosystem state, the biomass and therefore the catch of different species would vary. To capture the difference in value at different species composition of the catch, the node ‗average price per unit catch‘ is created. If the catch is constituted by highly valuable large reef fish then the average price per unit catch would be higher than if the catch is made up of lesser value species. The node ‗total fisheries revenue‘ represents the landed value28 received by the fishers for their catch; this node is dependent on the total fisheries catch and the average price per unit catch. 18.104.22.168 Tourism revenue The node ‗tourism revenue‘ is modeled to depend on the state of the ecosystem; it is expected that a highly restored ecosystem would be highly attractive to tourists while a poor and devastated ecosystem would bring less tourism benefits to the region (CisnerosMontemayor and Sumaila 2010). In this chapter, it was arbitrarily assumed that the revenue from tourism is directly proportional to the state of the ecosystem: that in partially restored ecosystems, the revenue would be 75% of the revenue in fully restored ecosystems, and 50% and 25% in ‗medium exploited‘ and ‗highly exploited‘ reef ecosystems, respectively. Several other factors might influence the income from tourism. A review of entrance fees in over 900 marine parks has shown that fees were mainly dependent on the ‗general perception of prices in any country‘ and lower fees were charged for parks with good quality reefs because the parks were located in poorer countries (Wielgus et al. 2009). The income was also dependent on coral cover and abundance of large reef fish (Wielgus et al. 2009) (but the analyses did not control for the effect of ‗general perception on price‘, so it was not possible to ascertain how the revenue 28 Here the landed value is used as a proxy for the economic benefits from fisheries. The landed value is chosen because of the limited information available on the costs associated with the different fisheries and limited information on how costs would change in the future years. 115 would change with change in reef condition). The revenue29 is constituted by an entrance fee to the park and payments for diving and other activities in the region (Erdmann pers. comm.). A percentage (40%) of the entrance fees is distributed among the villages as a compensation for use of the resource, a practice that has helped to reduce conflict elsewhere (Brunnschweiler 2009). About 34% of the total revenue from the tourism industry goes to the local community (Mark Erdmann pers. comm.). Hence 34% of the total projected revenue is used in the model, especially because this revenue is assumed to be a replacement for the fisher‘s revenue forgone due to fisheries restrictions. Two projections for increase in revenue from tourism are considered in Raja Ampat. Tourism Projection Low In year 2009, around 4,000 tourists (mostly foreign) visited the region. In the first scenario, the number of visitors increases to 10,000 per year after which it stabilizes around this number (Mark Erdmann pers. comm.). Compared to the number of visitors at the Bunaken National Park (~20,000 visitors per year), this is a relatively modest scenario for tourism increase in Raja Ampat. There are, however, a few reasons why tourism increase in Raja Ampat would be modest. Raja Ampat is a remote area—the nearest airport to access Raja Ampat is in the neighbouring Sorong Regency whereas Bunaken in Sulawesi is only an hour away from an international airport (Manado). Accessibility has been stated as a reason for the current lack of interest in some potentially attractive tourist spots in Indonesia (Tourism Indonesia 2010a). It is also considered that the water currents are higher in Raja Ampat (Tourism Indonesia 2008); therefore, only very experienced divers come to Raja Ampat (Mark Erdmann, pers. comm.). Tourism Projection High The second scenario is an increase in the number of visitors to Raja Ampat at a constant rate of 25% per year for 20 years. This projection is chosen because of three reasons: (1) 29 To be consistent with how the revenue from fisheries is modeled and because of lack of information on projected costs, the total revenue is used an indicator of the utility from tourism. 116 the projected increase for the next year is 1,000, which is about 25% increase from the visitors who arrived in the region in 2009 (~4000); (2) the regency is planning 3 airports in the region, which would increase the potential number of visitors, and there are also plans to set up an office of tourism industry in Bali for Raja Ampat; and (3) at this rate of increase, the maximum number of visitors arriving in Raja Ampat would be about 90,000 visitors per year in 20 years. This level is chosen here as an upper limit because of adverse ecological and social impacts of tourism (Harriott 2002) associated with very high number of visitors to a region. However, tourism could increase in Raja Ampat beyond this level (90,000 visitors per year), and the implications of higher than projected number of tourists is discussed later in the chapter. 22.214.171.124 Conservation interest Conservation interest is not modeled as a node; it is modeled in combination with tourism, for a highly restored ecosystem would be more attractive to conservationists, similar to tourism. The health of hard coral has been shown to be an indicator to evaluate ―human disturbance‖ (Fisher et al. 2008). Hard coral biomass is used as an indicator of progress in conservation. Conservation interest is modeled at two levels for Raja Ampat, both based on economic evaluation of natural resources in Raja Ampat (Dohar and Anggraeni 2007). Willingness to Pay (WTP) The ‗willingness to pay‘ (WTP) for an environmental service is usually determined by polling a group of people to judge how much they would spend for a specified goal like ‗improving reef management or committing to increased biodiversity for conservation‘ (Peters and Hawkins 2009). Measures of WTP of people in Raja Ampat were available from an economic valuation study conducted by Conservation International. Contingent valuation methods were used to calculate the WTP based on results of a survey conducted with the people of Raja Ampat (Dohar and Anggraeni 2007). The resulting indirect use value was estimated at about 1/30th of the fisheries revenue generated in the region. 117 Ecosystem Services (ES) In the same study Dohar and Anggraeni (2007) conducted a valuation of ecosystem services (ES) from the various ecosystems and found that the ‗total indirect use‘ values were about 5 times the direct use values (much higher than the ‗perceived value‘ measured using willingness to pay). The Raja Ampat regency has committed to improving fisheries management, tourism and conservation in its vision statement. The rationale for modeling conservation utility based on ES is that the Regency government, having committed to conservation, might value both the direct (fisheries and tourism) and indirect benefits (ES) from the coral reef ecosystem. 126.96.36.199 Utility The term ‗utility‘ originally belonged to the field of economics: it measures how a ‗particular attribute is valued‘ (Shotton 1999). This node combines, in essence, the different values and preferences associated with the use and maintenance of the ecosystem. Three competing interests are modeled by the ‗utility‘ node in the influence diagram. Fishers‘ utility is captured by the fisheries revenue. The utility of the tourism industry is modeled by tourism revenue. Finally, the conservation interests stated in the policy statements of the government and other conservation minded entities are modeled as the utility derived from a restored ecosystem. Three types of utility functions are considered in the chapter (Figure 5.3). Risk neutral utility refers to a linear utility function—the utility is directly proportional to the variable of interest; risk averse utility function increases at a decreasing rate with increase in the value of the variable and converges asymptotically; and the risk prone utility function increases exponentially with increase in the value of the variable. To model the risk averse function, the calculated utility is raised to a power < 1 (0.3 and 0.5) and to model the risk prone function, the utility is raised to a power > 1 (3 and 5) (see Figure 5.3). The following is a description of various reasons for differing utility functions among the different stakeholders: 118 Figure 5.3 General shape of utility functions used in the analysis Numbers show power value Utility of fisheries revenue It is considered that fisheries utility is predominantly linear. This utility function is depicted in the behaviour of fishers wherein they invest in bigger boats or equipment like motors to be able to catch more fish and increase their revenue. Fishers in Raja Ampat have increasingly integrated into the cash economy and have invested in similar measures to obtain higher catch and income from fisheries. This linear relationship can become a risk prone behaviour if the fisher is carrying a huge debt, because this might cause the fisher to take more risk to repay the loan. Also, a small component of fishers involved in destructive fishing might have a risk prone behaviour— their goal would be to maximise their revenue in each fishing trip without being detected or penalized by the management agency. For a section of the fishing community, which continues to adhere to ‗adat‘ (customary law), the utility from fisheries revenue could be risk-averse30. 30 Raja Ampat is located in the Papuan province in eastern Indonesia. In the 1960s, the Dutch ceded control over this territory and the administration was taken over by Indonesia. Before becoming part of Indonesia, fishing was governed by ‗adat‘ (customary law), and it was mostly for subsistence—the fisher communities were not integrated into the cash economy (Donnelly et al. 2003; Muljadi 2004). These situations changed when waves of immigrants from other parts of Indonesia arrived in the region under the influence of the government; Indonesians from other parts of the country were more integrated in the cash economy, and they did not recognize the ‗adat‘ (customary law) systems since everything was now supposed to be owned by the state (Goram 2007). In spite of these changes, there are other communities who have resurrected their customary laws and traditional management practices; these laws specify certain timing and gear use in fishing activity. Adherence to such customary laws referred to as sasi adat and sasi gereja (McLeod et al. 2009) indicates risk averse behaviour by the fishing community. The fishers also perceive that the biomass of large reef species and sharks has declined over the years (Ainsworth et al. 2008a). Also there are still fishing communities in the region that are not fully integrated into the cash economy; for such small-scale subsistence oriented fishers, the utility function would be risk prone because these fishers would not 119 Utility of tourism revenue The utility of tourism revenue in Raja Ampat, especially from the perspective of the Regency government is predominantly linear. This is reflected in the high interest and rapid development of the tourism industry in the initial stages of the industry from 2001 to 2005 (Mark Erdmann pers. comm.). The interest in developing the industry continues to increase. It is reflected in future plans, which include setting up an office in Bali to improve tourism in Raja Ampat and in the desire of the Raja Ampat Regency government to build airports in Raja Ampat (Tourism Indonesia, 2010b). However, communities associated with the Great Barrier Reef have not supported excessive growth in tourism due to adverse ecological and social impacts (Harriott 2002). High recreational use can damage coral reefs (Goreau 2009). The carrying capacity of coral reef for dive tourism is reported to depend on presence/absence of vulnerable species like coral reefs, training of divers, and presence of other anthropogenic stressors (review by Zakai and ChadwickFurman (2002), and several studies have reported limits on the number of dives per year at reef sites (Dixon et al. 1993; Schleyer and Tomalin 2000; Hawkins et al. 2002). So, it is possible that after a certain level of growth in tourism, the value from tourism might have a risk averse utility function. Utility of conservation The utility of conservation in Raja Ampat is perhaps predominantly linear. The programs for conservation were initiated by COREMAP, The Nature Conservancy, Conservation International, World Wide Fund, almost all began in the early 2000s. At that time destructive fishing was more prevalent in the region than at the present; influenced by the awareness generated by these programs, several fishers have discarded these methods. With the increase in awareness, the NGOs began campaigning for marine protected areas and a network of MPAs was successfully established in 2007 in Raja Ampat. Today efforts continue to improve spatial zoning inside the MPAs. The consistent effort for improving conservation reflects linear utility for conservation. However, a risk averse regularly catch more than what is required for consumption in the fishing village—after which any surplus is not useful. 120 conservation utility31 can also be argued for on the grounds that the established goals of various management agencies and NGOs for setting up MPAs are in the range of closing 20% to 30% of the marine area (Hoegh-Guldberg 2006; Ban 2008; Olsson et al. 2008) (after these goals are achieved, there might not be interest in closing larger areas). For conservation groups specifically interested in the protection of charismatic species (turtles, whales, etc.) the conservation utility is probably risk prone32 because every single turtle or whale rescued results in increasingly higher satisfaction. The different stakeholders are modeled using predominantly linear (risk neutral) functions. In the real world, the utility functions are more complex. This complexity is addressed to a limited extent by modeling each stakeholder with the other predominant utility functions: high risk averse, low risk averse, high risk prone and low risk prone. The goal here is to map the range within which the results would vary under different formulations of utility functions; a better understanding of utility functions would have to be based on surveys of the respective stakeholders. There are a few reasons to adopt this approach in the analysis: (1) A Risk neutral function is easy to model and allows an easy comparison of the different variables in consideration; (2) Risk neutral behavior is midway between the extremes of risk prone and risk averse behaviour (Binmore 1992) and so is a better choice as a base model; and (3) subsequent modeling of risk averse and risk prone behavior show how the difference in utility influences the results and provides a better understanding of the influence utility functions have on the results. Similar assumptions of risk neutral behavior have been made for the sake of simplicity in fisheries analysis (McKelvey et al. 2007). 31 Similarly, several biological reference points used as thresholds in fisheries management (Mace 1994; Hilborn 2002) and the reference points trigger responsive measures only when the biological indicators approach these thresholds. 32 Risk prone conservation utility is also shown by the general public when they may be willing to pay to protect a well-known diverse region, but may not be interested in trying to conserve a less diverse ecosystem because they do not see the value of protecting a system that is very depleted or they believe that the system will not recover. 121 5.2.3 Discounting Usually the benefits in the future are not valued the same as benefits in the present (Clark 1973), and this ‗time preference‘ or ‗impatience‘ is captured by discounting (Clark 1990; Sumaila 2004). The higher the discount rate, the higher current benefits are valued relative to the future ones. Different levels of discounting favour different ―time streams‖ of benefits and so have considerable impact on the policy choice (Berman and Sumaila 2006). In the analysis, fisheries revenue, tourism revenue, and conservation benefits, each are discounted at four rates 3%, 7%, 10% and 24% for 20 years. The four levels of discount rate are taken from different sources. The 3% discount rate is used as a proxy for intergeneration discounting: this is based on the findings in Sumaila (2004) that conventional discounting rates between 0 to 3% give similar results as intergenerational discount rates. Bailey (2007) uses 7% discount rate in a principle agent analysis of destructive fishing in Raja Ampat. The discount rate 10% is obtained from economic valuation of natural resources in Raja Ampat (Dohar and Anggraeni 2007). A discount rate of 24% is obtained from Buchary (2010), and it is the official social rate of discounting for Indonesia. The net present value (NPV) from the flow of fisheries revenue, tourism revenue, and conservation benefits in the next 20 years is calculated as follows (Sumaila and Walters 2005): Discount factor d 1 where, r is the discount rate 1 r Weight on benefits in each year: Wt d t T Net present value: NPV VtWt t 0 Where, Vt is the revenue in a given year t, and T denotes the total number of years over which the discounted benefits are calculated. Figure 5.4 shows the flow of discounted benefits with change in time. 122 Figure 5.4 Decay of future benefits at different discount rates used in the analysis. Area under the curves corresponds to net present value. 5.3 Results The results are analyzed to see which scenarios are favored depending on different stakeholders and different specifications (linear or non-linear) of the utility functions considered in the analysis to see if any scenarios emerged as robust under the existing sources of uncertainty. Linear utility functions are explored first followed by non-linear utility functions. 5.3.1 Linear utility functions If fisheries revenue is the only source of utility (Figure 5.5) or the only source of revenue for the regency management, then implementing marine protected areas is not a favored option. In this case, scenarios with minimum restrictions on fisheries are favored. For a highly exploited ecosystem, the utilities of status quo fishing and minimum restrictions on fisheries are very similar. When the ecosystem state is ―medium 123 exploited‖, ‗restricting net fisheries‘ and implementing a ‗25% closure‘ are observed to be slightly better than the status quo scenario. When the starting ecosystem state is ―partially restored‖, ‗restricting net fishing‘ and ‗no shark fishing‘ appear to be the best options. Figure 5.5 Utility of fisheries revenue. Fisheries revenue is modeled as a linear function. The three panels show the results for the three starting ecosystem states. The vertical axis shows the relative difference in utility (the scenario with the minimum utility in any ecosystem state is assigned a value 1 and the remaining scenarios are shown relative to that scenario). The abbreviations for each bar describe the restoration scenario (SQ Status Quo, ND No destructive fishing, NL No live fish fishing, NN No Net fishing, NS No shark fishing, C25 25% closure, C50 50% closure, C75 75% closure) When all the weight is placed on tourism revenue (Figure 5.6), and the ecosystem is ―highly exploited‖, then the most favored policies are fishing closures using MPAs, with 75% closure observed to be twice as good as fishing gear restrictions. The favored policies are the same for ―moderately exploited‖ and ―partially restored‖ ecosystems, but with improvement in ecosystem state, the difference in utility between the best and worst options declines. The response is similar whether the revenue from tourism is modeled according to the ‗low‘ or ‗high‘ scenario. 124 Figure 5.6 Utility of tourism revenue. Tourism revenue is modeled as a linear function. The three panels show the results for the three starting ecosystem states, and the abbreviations for each bar describe the restoration scenario (SQ Status Quo, ND No destructive fishing, NL No live fish fishing, NN No Net fishing, NS No shark fishing, C25 25% closure, C50 50% closure, C75 75% closure) Modeling conservation utility alone, with all non-negative values for utility (Figure 5.7), favors fishing closures across all ecosystem states. For ecosystem state ―highly exploited‖, the protection scenarios are more strongly favored than for ecosystem states ―medium exploited‖ and partially restored‖. The reason for the observation is that there is a higher increase in conservation benefit from protecting a ―highly exploited‖ ecosystem than protecting a ―medium exploited‖ or partially restored‖. The results obtained are very similar whether conservation is modeled according to WTP or ES. Modeling conservation utility alone, with a negative value for low biomass for hard coral, also favors fishing closures across all ecosystem states. However, 75% closure of a ―partially restored‖ ecosystem would be probably only realistic when considering waters around uninhabited islands where the fishers would not prefer to fish. Figure 5.7 Utility from conservation benefits. Conservation benefits are modeled as a linear function (non-negative) in the top panel. In the lower panel conservation benefits are modeled with linear (negative values for depleted ecosystem) utility function. 125 When fisheries and tourism revenue contribute to utility, the MPAs become more favorable than when only fisheries revenue was considered, and less favorable than when tourism alone contributed to utility. If the tourism scenario ‗low‘ is considered, and ecosystem state is ―highly exploited‖, then ‗status quo‘ and minimal fishing restriction scenarios are favored. For ―moderately exploited‖ and ―partially restored‖ ecosystem states, the most favored scenarios are restricting net fisheries and MPA scenario with 25% closure. In the model, it was assumed that in partially restored ecosystem, the tourism revenue would be 75% of the tourism revenue from a fully restored ecosystem, and 50% and 25% in ‗medium exploited‘ and highly exploited‘ reef ecosystems respectively, and the results are sensitive to this assumption. For ―highly exploited‖ and ―medium exploited‖ ecosystems, the tourism revenues (low) are lower compared to fisheries revenue. So, for ―highly exploited‖ and ―medium exploited‖ ecosystems, the scenarios with highest utility are similar to the scenarios that had the highest utility when utility from fisheries alone were considered. A change in the preferred scenario is seen only when the ecosystem state is ―partially restored‖ because the tourism revenue from a ―partially restored‖ ecosystem is higher. If tourism is expected to follow scenario ‗high‘, then across all ecosystem states, ‗restricting net fishing‘ emerges as the most favorable scenario. For ―highly exploited‖ ecosystem state, ‗restricting net fishing‘ is only slightly better than scenarios with minimum fishing restrictions. For ―moderately exploited‖ and ―partially restored‖ ecosystem states, the second best scenario is ‗25% closure‘. When fisheries and tourism revenue and conservation benefits contribute to utility, 24 alternative combinations of results can arise; these combinations derive from ecosystem state, tourism modeled according to low or high scenario, and conservation modeled according to WTP or ES. Because of the large number of combinations, the results showing the two best scenarios for each utility combination are shown in Table 5.1. When conservation is modeled based on WTP, the results are very similar to the results obtained when only fisheries and tourism were considered. The reason is that conservation modeled as WTP is only about 1/30th of the revenue from fisheries and so 126 has only a small influence on the results. When conservation is modeled according to ES, then the conservation benefits override all other sources of utility—fisheries revenue, low or high tourism revenue—and consistently favor 75% fishing closure33. Table 5.1 Utility from fisheries revenue, tourism revenue, and conservation benefits when all the three sources of utility are modeled with linear utility functions The abbreviations describe the restoration scenario (SQ Status Quo, ND No destructive fishing, NL No live fish fishing, NN No Net fishing, NS No shark fishing, C25 25% closure, C50 50% closure, C75 75% closure) Conservation WTP ES Ecosystem states Tourism Linear - non Linear Linear - non negative negative negative Linear negative Highly exploited Medium exploited Partially restored Highly exploited Medium exploited Partially restored C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 Low High SQ NS NN C25 NN C25 NN SQ NN C25 NN C25 SQ NS C25 NN C25 NN NN SQ C25 NN NN C25 C75 C50 C50 C25 C75 C50 C75 C50 C50 C25 C75 C50 5.3.2 Discounting When only the fisheries revenue contributes to utility, the NPV from status quo and minimum fishing restrictions scenarios are greater than the NPV from protection scenarios because the revenue from protection scenarios increases later in the time period (from rebuilding fish populations). Protection scenarios are less favoured at high discount rates. When fisheries revenue and tourism contribute to utility, the NPV of both fisheries and tourism revenues decline in comparison to undiscounted revenues, but the tourism revenues are more affected than fisheries revenues (Figure 5.8). The result is observed because tourism in Raja Ampat is a developing industry, and a greater portion of the 33 There is a big difference between modeling conservation as WTP or ES. WTP measures what amount an individual is willing to pay and therefore will usually be a value lower than his/her income. When income comes from direct benefits from use of a resource, this value tends to be considerably lower than ES which combines both direct and indirect benefits. 127 revenue is expected in the later years after the industry becomes fully established. The contribution from tourism revenue to the decisions diminishes; notice, as the discount rate increases, the tourism NPV becomes a smaller fraction of the total NPV. Because the relative contribution of tourism to fisheries revenue declines, the scenarios preferred are similar to scenarios preferred when fisheries revenue is modeled as the only source of utility. Therefore, when discount rates are considered, fishing restrictions (i.e., restoration strategies) are not favoured; this influence on decision is more predominant at higher rates of discounting. When conservation benefits are also added to the calculations of utility, the results obtained are similar for conservation modeled according to the WTP because the values of WTP for conservation benefits are very small (1/30th) in comparison to fisheries revenues. Conservation modeled as ES consistently favours protection scenarios even at the highest discounting rates. Figure 5.8 Comparison of discounted benefits from fisheries and tourism. The horizontal axis shows different discount rates. The vertical axis shows the ratio of discounted benefits to undiscounted benefits. 128 5.3.3 Non-linear utility functions In the analysis, it is assumed that the utility of all the stakeholders is predominantly linear and that some stakeholders within each group could have non-linear utility functions. Here, the implications of non-linear utility functions are briefly explored. Fisheries risk-averse utility function If the utility of fisheries and conservation are modeled as risk averse functions, then it is difficult to choose between different restoration scenarios because all the scenarios have very similar values of utility. Whether the policy favors scenarios with slightly less or more fishing mortality depends on the power on the utility functions—if the power on the fisheries revenue is lower (0.3) than the power on the conservation utility (0.5), then the utility values slightly favor conservation and vice versa. If the utility of fisheries are modeled as risk averse function and tourism-conservation combination as risk prone function, then all scenarios that allow high levels of conservation (setting up MPAs and restricting net fisheries) are very highly favored compared to status quo or minor fishing restrictions. For a ‗highly exploited‘ ecosystem, depending on whether conservation is modeled based on WTP or ES, the factor by which 75% closure is better than the worst choice maybe 11 to 178 times (power on conservation utility equals 3) or 60 to 14000 times (power on conservation utility equals 5). For a ‗medium exploited‘ ecosystem, the utility for the MPAs is about 2 to 6 times the scenario that results in least utility, depending on the model used to value conservation benefits (WTS or ES) and power on conservation utility function (3 or 5). Similarly MPA scenarios have the highest utility when the ecosystem state is ―partially restored‖. The power on the utility function affects the degree to which one decision is favored over the other (e.g., 3 times vs. 8 times), but it does not change the decision. 129 Fisheries risk prone utility function If fisheries utility is modeled as a risk prone function and tourism-conservation combination using risk averse function, then scenarios with minimal restrictions are preferred across all ecosystem states. The results are interesting when both fisheries and tourism-conservation combination are modeled as risk prone utility functions. The results are illustrated in Table 5.2. Table 5.2 Utility from fisheries revenue, tourism revenue, and conservation benefits. All are modeled with risk prone utility function. The table shows the scenarios which result in highest utility values. The abbreviations describe the restoration scenario (SQ Status Quo, ND No destructive fishing, NL No live fish fishing, NN No Net fishing, NS No shark fishing, C25 25% closure, C50 50% closure, C75 75% closure) Exponent on Conservation (WTP or ES) and Tourism (Low or High) 3 5 3 5 Exponent on Ecosystem fisheries WTP & states utility Low WTP & High WTP & Low WTP & High ES and Low ES and High ES and Low ES and High Highly exploited SQ NS C75 NN C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 NN C25 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 Partially restored NN C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 C75 C50 Highly exploited SQ NS SQ NS SQ NS C75 SQ SQ NS SQ NS C75 C50 C75 C50 NN C25 NN C25 NN C25 C75 C50 NN C25 NN C25 C75 C50 C75 C50 NN NS NN NS C75 C50 C75 C50 NN NS NN NS C75 C50 C75 C50 Medium exploited Medium exploited Partially restored 3 5 When conservation is modeled using ES (for tourism-conservation combination), then irrespective of the exponent on the utility functions, the results always favor setting up the largest MPA, except when the exponent on fisheries utility (5) is greater than the exponent on tourism-conservation utility (3). When conservation is modeled using WTP 130 (for tourism-conservation combination), but the exponent on conservation utility is higher than the exponent on fisheries utility, then again the highest levels of protection of the ecosystem are favored. When conservation is modeled using WTP (for tourismconservation combination), but the exponent on conservation utility is smaller than the exponent on fisheries utility, then scenarios that allow for minimum fishing restrictions are favored for a ―highly exploited‖ ecosystem. For a ―medium exploited system‖, ―no net fishing‖ and ―25% closure‖ are favored, and for a partially restored ecosystem, ―no net fishing‖ and ―no shark fishing‖ are favored. When the exponent on both the fisheries and conservation utility is the same (3 or 5), then the results are dependent on the projections for tourism industry. When the projection for tourism is ‗high‘, then protection scenarios are favored. When tourism projection is ‗low‘ and the ecosystem is ―highly exploited‖, the results favor minimal fishing restrictions, and in ―medium exploited‖ ecosystem the results favour protection measures such as ‗no net fishing‘ and ‗25% closures‘. In ―partially restored‖ ecosystem the scenarios ‗no net fishing‘ and ‗50% closure‘ and ‗75% closure‘ are favoured. Thus, non-linear utility functions can considerably influence the results obtained from the analysis. 5.4 Discussion 5.4.1 Policy choice When only one type of stakeholder—the fisher—is considered the scenarios that maximize fishing revenues are favored. When other stakeholders, tourism industry and conservationists are included, protection scenarios become increasingly favorable. Other studies have indicated that high tourism revenues have the potential to improve coral conservation (White et al. 2000; Depondt and Green 2006) and that alternative source of incomes can reduce the opposition to reserves (Smith et al. 2010). When tourism is modeled according to scenario ‗low‘, then for a highly exploited ecosystem, the ‗status quo‘ is scenario is preferred. The same result is observed even when conservation (WTP) is added to the utility. Similar results are observed when fisheries are modeled with risk prone utility function. The reason for this difference in choice based on the state of the 131 ecosystem is that the tourism benefits for a highly exploited ecosystem even when restoration efforts are made are not high enough to replace the revenue lost from fisheries. Higher levels of tourism revenue (more than scenario ‗low‘) or conservation benefits (more than WTP) are able to offset losses in fisheries and make MPAs a favorable decision also for highly depleted ecosystems. Thus, when industries that depend on a conserved ecosystem are brought into the decision making process, then policies that prevent ecosystem degradation and favor protection can be seen as preferable. From the perspective of a decision maker, it is easier in this case to arrive at a compromise between different stakeholders because losses in one source of revenue are compensated by gains in the other sources. The results suggest that the most robust policies are restricting net fisheries in Raja Ampat followed by 25% closure. Here by using Ecosim, the habitat choices associated with MPAs is ignored; a much more rigorous analysis would involve spatial models of ecosystems in different states of exploitation. These policies are favored in most situations modeled using linear utility for a medium exploited, partially restored, and highly exploited ecosystem when the tourism is modeled according to scenario ‗high‘. Even when the utilities are modeled using risk averse and risk prone functions, no net fishing and 25% closure are the favored policies for ‗medium exploited‘ ecosystem. No net fishing is also a favored policy in several instances for a partially restored ecosystem. It is an interesting result that the robust scenario for MPA is similar to 20 to 30% closure based on other research (Gaines et al. 2010). These policies compromise the utilities of the fisheries revenue, but the scenarios emerge as robust because the gains in tourism revenue and conservation benefits compensate the losses in fisheries revenue. 5.4.2 Discounting When discounting is included in the calculations of the net benefits, then restoration scenarios become less favourable. The ‗twofold‘ problems with discounting net benefits of marine restoration identified in Berman and Sumaila (2006) are highlighted in this chapter. The first is valuing only the ‗production‘ or direct benefits from the system, and the second is the lower value attributed to future benefits from a restored ecosystem. 132 When indirect benefits are modeled by including conservation benefits (ES) in the utility, then protection scenarios are favoured. Global survey of ecosystem services has shown that use values are only a small percentage of the total value from an ecosystem (Costanza et al. 1997; Jansson et al. 1999; Boumans et al. 2002). Not accounting for nonuse values from an ecosystem ignores the ―cost resource depletion imposes on future generations‖ (Howarth and Farber 2002). Though quantification of ecosystem services is not straightforward (Beaumont et al. 2007; Meyerson et al. 2008), the results in this chapter show that including non-use values would significantly influence the cost-benefit calculations of marine management policy in favour of restoration. When only direct benefits are considered, scenarios that provide maximum benefits from fisheries in the short-term are preferred. The preferences for short-term benefits are greater at higher discount rates. Four discount rates are used in the analysis, but higher discount rates may be more appropriate in an evaluation of strategies for coral reef ecosystems in developing countries considering that poverty among fishers is associated with high discount rates (Pauly et al. 1989; Sumaila 2003). If, in addition to the time preferences of the current generation, the perspective of future generations are included in the evaluation (~3% discount rate), then long-term benefits become more favourable. Stern and Taylor (2007) advocate low discount rates in valuation of ecosystems because unlike ‗roads or railways‘, ecosystems would be valued ‗as long as the planet and its people exist‘. Economic evaluations using high discount rates on direct benefits will by their nature not support restoration strategies; including industries that depend on sustainable use of the ecosystem in the calculations of benefits favour restoration strategies. 5.4.3 Utility functions It is observed that when the utility is raised to powers less than 1 (risk averse function), then only very big changes in the variable are captured by the utility function. Modeling the utility with power > 1 leads to the opposite result; small changes in the variable are magnified. For example, when revenue from fisheries is modeled as a linear function, then the highest utility is twice the value of the lowest utility; when the power on the utility function is 0.3, the highest utility value is only 26% better than the lowest utility value; when the power on the utility function is 5, the highest utility value is 27 times the 133 lowest utility value. So risk averse utility functions should be used when it is expected that the variable in question will show a high response to change in the model, but the change needs to be dampened in the utility function (for example, in a model, the plankton biomass might change by higher orders of magnitude than marine mammals, and the modeler might wish to reduce the influence of plankton biomass). Risk prone utility functions should be used when small changes need to be magnified (for example, in a climate model, small changes in temperature could have a high significance compared to other changes in the model). A linear utility function should always be modeled for 2 reasons: (1) it can be used to observe the trend in utility from different sources; and (2) it can be used to error check the model. 5.4.4 Tourism revenue In this analysis, tourism and fisheries revenue have been viewed as interchangeable sources of revenue to the community. However, one of the criticisms against the tourism industry is that local people are often sidelined, and only a small portion of the revenues ―trickle down to the local population‖ (Dixon et al. 1993). In Raja Ampat, the local community receives about 34% of the revenue; it seems that the tourism development is progressing in an equitable direction. About 4 years ago, the local people in Raja Ampat were not familiar with visits of tourists, the villagers did not speak English, and the general perception was that because of these difficulties, they would not be able to adapt to tourism as an alternate source of revenue. It seems to have changed, with several diver resorts reporting that tourists will find people conversant in English, Spanish, and other foreign languages; it might change further in future years when the younger, more educated individuals of Raja Ampat enter the industry. The process for marine management including fishers and tourism adopted in Raja Ampat is the same as the model developed in Bunaken National Park through a process that evolved after a long process of consultation and involvement of the different stakeholders. The proportion of tourism revenue received by the local population might then increase to above 34%; however, this possibility is not considered in the chapter. Tourism growth is modelled for two extreme ‗low‘ and ‗high‘ scenarios based on the best information available. The variation is shown by the results obtained under the two extreme scenarios. Additional 134 work is needed to build more realistic models for growth of tourism in Raja Ampat because tourism will depend on several factors ranging from perception of health and safety in the region, ease of access, visa and other regulations, local support, and general political stability, etc. 5.4.5 Conservation utility If conservation benefit is modeled based on ecosystem services, then this source of utility completely over-rides the other sources of utility showing that the value of the indirect benefits from the ecosystem are higher than direct extractive benefits. However, it is unrealistic to forego the direct benefits for the indirect benefits. The utility from the fishing industry is not only the revenue from fish catch, but it also a source of food (Brunner et al. 2008). Our dependence on food, as one of the most basic needs of life, cannot be captured by a utility function based on fishing revenues, but would probably need a different metric (for example, caloric needs). 5.4.6 Other anthropogenic impacts The Ecopath with Ecosim model is able to capture the changes in biomasses of fish population based on changes in fishing effort. However, it is not able to capture other changes because of development of other industries. Interest in the mining industry could result in siltation and poisoning due to tailings which could lead to habitat changes and detrimental effects on fish populations. These sources of variation are not considered in the model. 5.5 Conclusion The analysis is able to successfully identify two policy options for restoration that are quite robust against the uncertainty surrounding complex utility functions of the various stakeholders. The scenarios are restricting net fisheries and 25% closure. Bringing multiple stakeholders into the decision making process is not easy—In Bunaken National Park, it took several years of effort and cooperation, and entailed several failures before a management method was identified that was considered equitable by all stakeholders, the fishers, tourism industry, local management body, and tourists. Building a model of the 135 utilities of the different groups can speed up the process by providing scenarios that are at least theoretically robust to expected uncertainty and differences in utility functions between groups. Socially successful restoration strategies influence long-term ecological success (Christie 2004); therefore model-based studies like this, which can explore equitable resource allocation and use, can provide useful directions for testing actual responses in the field. 136 5.6 References Ainsworth C. H., T. J. Pitcher, and C. Rotinsulu. 2008a. Evidence of fishery depletions and shifting cognitive baselines in Eastern Indonesia. 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Impacts of intensive recreational diving on reef corals at Eilat, northern Red Sea. Biological Conservation 105(2):179-187. 143 6 The Influence of Life History Parameters in Fish Population Restoration34 6.1 Introduction Increase in fishing mortality has led to changes in fish community structure; fishing of forage fish has negatively affected the abundance of birds and marine mammals (Jennings and Kaiser 1998). In communities with top predatory fish species, the phenomenon of ―fishing down the food web‖ (Pauly et al. 1998)––smaller faster growing species increasingly dominate the fish catch. It has been estimated that 90% of the large predatory fish have declined (Christensen et al. 2003); several fish stocks have declined (Myers et al. 1996; NAS 1998; Rose and Kulka 1999; Morris et al. 2000; Dulvy et al. 2003; Hutchings and Reynolds 2004; Mullon et al. 2005). Large scale transitions and alternate ecosystem states have been observed (Daskalov et al. 2007). Fish communities have shifted towards relatively homogenous states (Jackson 2008). Several studies reported that large slow growing species declined faster than small fast growing species (Jennings et al. 1999; Ault et al. 2005). International conventions have promoted marine protected areas (MPAs) to restore marine biodiversity (Spalding et al. 2008; Halpern et al. 2010), and as of 2008, about 1 million km2 (4.9%) of the total continental shelf area was reported to be protected (Spalding et al. 2008). MPAs are valuable sites for empirical studies on species recovery. One of the ―primary‖ results of protection was an increase in fish body size inside the MPAs (Tetreault and Ambrose 2007; Anticamara et al. 2010). Studies on reef fish recovery in MPAs in Kenya showed that recovery patterns were different among different families and size classes (McClanahan et al. 2007). Metaanalyses of recovery inside MPAs found, ―large fished species responded strongly to protection and small fished species showed weaker responses‖ (Molloy et al. 2009); 34 A version of this chapter will be submitted for publication. Varkey, D. A., Pitcher, T. J. The influence of life history parameters in fish population restoration. 144 similarly, higher trophic level fish showed greater responses to protection (Lester et al. 2009). Some recovery is due to habitat restoration, which is not considered in this chapter. The available empirical studies suggested relationships between recovery and life history, but at the same time, recovery of a species could extend over a few years or decades (Halpern 2003; Russ and Alcala 2004). Therefore, there is value in pursuing the influence of life history on recovery; the greatest advantage is the ability of ―demonstrating broad trends across species‖ (Vetter 1988). Jennings et al. (2008) combined life history theory with macroecology and food web ecology to predict potential fish biomasses and global trophic structure. Basic life history information is available for a large number of fish species (courtesy: Fishbase); therefore, any broad generalizations reached can be extended over a wide range of species (for example, the use of life history to predict natural mortality in fish Pauly 1980; Hoenig 1983; Vetter 1988). Understanding the relationships between ―life history traits and population dynamics is a central goal in ecology‖ (Goodwin et al. 2006). Fish life history studies show that short-lived species have a higher population growth rate than long-lived species (Jennings et al. 1999; Denney et al. 2002). It has been suggested that life history parameters such as maximum size (Jennings 2000) and age at maturity (Myers et al. 1997; Denney et al. 2002), could be used to predict population recovery rates. In this chapter, the influence of life history parameters of fish species on the potential magnitude of recovery (not the recovery rate i.e. slower or faster recovery) of fish populations is explored. Two factors determine the magnitude of recovery of any fish population; biomass per recruit and increase in the number of recruits. The biomass per recruit is calculated at various levels of mortality on the fish. Analysis of recruitment is not straightforward because of the compensation in recruitment that occurs at low population levels; the basic phenomenon that is modeled using Beverton-Holt (Beverton and Holt 1957), Ricker (Ricker 1954) and other stock recruitment models. There are five main difficulties associated with estimating the shape of the stock recruitment relations: (1) the curvature of the stock recruit relation emerges only when the population has declined to low levels 145 of stock size; (2) studies, therefore, require long time series of spawner and recruit data (He et al. 2006); (3) the available data show variability and noise in recruit data (Rothschild 2000; He et al. 2006); (4) errors due to time series bias and errors in variable case from error in measurement of spawner data (Walters and Martell 2004); (5) overcompensatory and depensatory responses (Liermann 2001). To analyze the change in mean recruitment with mortality, an index is devised which can be used to express the mean recruitment at any level of total mortality as a percentage of the unfished recruitment. This allows comparison of recruitment in different species at different levels of mortality on a scale from 0 to 100. Then two extremes are assumed for the range within which the recruitment compensation could vary for most species. Finally fish species are grouped according to their life history parameters to explore any overarching patterns in recruitment that are influenced by the life history parameters of the fish species. 6.2 Methods The patterns in biomass per recruit and number of recruits predicted by standard fisheries assessment models are explored. In the analysis of both biomass per recruit and recruitment, several simplifying assumptions were made. Later, in the results and discussion sections, the implications of the assumptions are discussed. The analysis of recruitment is based on growth parameter data and natural mortality data for around 1800 species obtained from the Fishbase database (Freose and Pauly 2010). 6.2.1 Biomass per recruit (B/R) The biomass per recruit (B/R) is based on the classic ―steady state model‖ of Beverton and Holt (1957) that describes the state of the stock and the catch. The steady state model is used in a situation where the current level of fishing pressure has been consistent for a long time such that all the fish alive have been exposed to it since they recruited. Hence this chapter is restricted to the analysis of equilibrium conditions and cannot predict time dynamic changes in the species biomass per recruit. The biomass per recruit model expresses the annual average biomass of the exploited part of the cohort. 146 1 exp(( M nk )(tc tr ) (exp((m nk ) * (tc tr )))* (1 exp(((F M nk ) * (tl tc )))) B n 3 W n 0 U n [exp nk tr t0 ][ R M nk F M nk where, F = instantaneous rate of fishing mortality, M = instantaneous rate of natural mortality, R = number of recruits, W∞ = asymptotic weight (grams) calculated as: a*L∞b, where a and b are the length-weight parameters, L∞ is the asymptotic length (cm) of the fish, k = von Bertalanffy metabolic growth coefficient (yr-1), t0 = the initial condition parameter is the theoretical age (yr) at which fish have zero length, tr = age (yr) at recruitment to fishable stock, tc = actual age (yr) at first capture with given gear, tl = maximum age (yr) of fish in stock, and Un = integration constant necessitated by use of the von Bertalanffy growth model, U0 = 1, U1 = -3, U2 = 3, U3 = 1 (n is only an index used to denote the 4 values of U). For a detailed description of the model and the underlying assumptions, please refer to the fisheries classic ‗Beverton and Holt (1957)‘. B/R can also be modeled using an equilibrium age structure model. The above described formulation is chosen because it requires fewer parameters. The parameter L∞ is not needed in the model. In the analysis, the t0 is assumed to be -1. The W∞ is assumed to be 10,000 g in the calculations. The tl of the fish is assumed to be 10 years. The implications of these assumptions of asymptotic weight and maximum age are discussed later. The age ‗10 years‘ is chosen because roughly 50% of ~1800 species from Fishbase used in the analysis have a maximum age ‗10 years‘ or lower. It was also assumed that tr and tr are equal to 1. Thus the calculations represent fish populations fully vulnerable to fishing pressure from age 1 onwards. The B/R estimates are calculated at specific values of average annual survival rate and any trend in the relationship between B/R and survival rates were observed. The survival rates range from (1 to 95% per year) and correspond to instantaneous annual total mortality (Z) range from 4.6 to 0.05. 6.2.2 Recruitment Recruitment is modelled in the analysis using the Beverton and Holt stock recruitment (BH-SR) relationship because it is a very common representation of stock recruitment relationship; additionally, BH-SR relationship is easier to parameterize and discuss compared to other stock recruitment relationships. The BH-SR curve is described as: 147 R S (1 S ) where, R = recruitment, S = breeding stock size. The parameter α is the slope at the origin of the function and represents the maximum recruits per unit breeding stock size at low stock size. Using this equation, the curve approaches an asymptote equal to α/β at increasing levels of spawning stock biomass. ―Parameter α increases the height of the asymptote, and β increases the rate of approach to the asymptote‖ (Jennings et al. 2001). 188.8.131.52 Unfished recruitment R0 The unfished recruitment R0, denotes the number of recruits in an unfished population. The standard equation35 (Walters and Martell 2004) for calculation of R0 is: R0 (e0 1) e0 where, e 0 the fecundity incidence function represents the fecundity per recruit in an unfished population. The фe0 is calculated as the sum over ages of unfished survivorship and fecundity at age. The survivorship at age (lage) is the probability of surviving to each age and is calculated from estimates of mortality (natural mortality (M) is used to calculate the unfished survivorship). Unfished Survivorship: At age =1, l age 1 At age > 1 lage l( age 1) exp( M ( age 1) ) 35 The formula for R0 is derived by integrating the area under the curve across the numbers at age, fecundity at age, and relative effect of density-dependent mortality at each age; the parameterization is based on incidence functions (Botsford 1981). Incidence functions are used to describe any property on a per recruit basis. The incidence functions have been further updated to calculate analytical relationships between the parameters (α and β) of the BH-SR function and other parameters R0, E0 (unfished eggs per recruit) (Walters and Martell 2004). 148 The fecundity at age is calculated as the sum-product of weight at age and maturity at age. Weight at age: Wage a(lage ) Maturity mata g e b 1 (age agema t ) 1 exp sig The factor sig in the above equation refers to the shape of the maturity at age curve, and it can be understood as the standard deviation around the age at maturity. At small values of sig (0.2), the maturity at age curve is steep (almost knife-edge at age at maturity); at high values (~5) the curve almost becomes linear with age. The sig is chosen to be 10% of the age at maturity; the value is arrived at after observing the plots of maturity schedule for several combinations of age at maturity and the ratio of sig to the age at maturity. Fecundity feca g e wa g e.mat a g e Unfished Fecundity per Recruit e 0 fec a g e.l a g e The R0 can be expressed as recruitment relative to the asymptote of the Beverton- Holt SR curve. As an arbitrary situation, let us assume that the R0 is 95% of the asymptote. (e0 1) e0 0.95* / e0 1 e0 *0.95* / e 0 1 0.95e0 e 0 20 149 Hence, the R0 is 95% of the asymptote when the product of the α parameter and the фe0 is 20. The index (100/ e0 ) (here 100/20 =5) represents the percentage difference between R0 and the asymptote. Therefore, 100(100/e0 ) =100-5=95 expresses R0 as a fraction of the asymptote. As the value of α.фe0 increases above 20, the R0 approaches the asymptote; if α.фe0 is 50, then ratio of R0 is 98% of the asymptote. Interestingly the product α.фe0 calculated when a population is unfished (total mortality equals natural mortality) is referred to as compensation ratio. This index is referred to by several notations: CR (Goodwin et al. 2006; Forrest et al. 2008), â (Myers et al. 1999), and Κ (kappa) (Martell et al. 2008; Walters et al. 2008); in this chapter it is referred to as CR. The index was originally proposed and named by Goodyear 1977), and it measures ―the relative improvement in juvenile survival at low stock sizes‖ (Forrest et al. 2008). It is the ratio of the juvenile survival at very low stock size and the juvenile survival at unfished stock size. CR can be calculated as the product of α and фe0, since the reciprocal of фe0 represents juvenile survival at unfished stock size (Forrest et al. 2008). The CR has also been defined as the product of α and unfished spawners per recruit (SPRF=0) (Myers et al. 1999). But, as explained in Forrest et al. (2008), when the ―relative fecundity is described as the product of mean weight-at-age and maturity-at-age, the фe0 is the same as SPRF=0. 184.108.40.206 Extending the algebra to mean recruitment levels below R0 The mean equilibrium recruitment at any level of total mortality for a Beverton-Holt SR curve is calculated by the equation (Walters and Martell 2004): R e (e 1) e This equation is the same as the equation for R0 except that unfished fecundity per recruit (фe0) is replaced by fished fecundity per recruit (фe). The фe is calculated using the same equations as фe0 except that in the calculation of survivorship, natural mortality (M) is replaced by total mortality (Z). 150 Fished Survivorship: At age =1 l f 1 At age> 1 l f l f ( age 1) exp(Z ( age 1) ) Weight at age: Wage a(lage ) Maturity mata g e b 1 (age agema t ) 1 exp sig Fecundity feca g e wa g e.mat a g e Fished Fecundity per Recruit e fec a g e.la g e More details about the function and the formula can be obtained from Walters and Martell (2004). Again, the index e can be used to express mean recruitment as a percentage of recruitment at the asymptote of the Beverton-Holt SR curve. As an arbitrary situation, let us assume that the mean recruitment is 90% of the asymptote (similar to the earlier formulation) then: (e 1) e 0.9 * / e 10 According to the equation, a 10% change from the mean recruitment happens when the product of α and фe equals to 10. The index (100/ e ) represents the percent decrease in equilibrium recruitment from the asymptote. Thus, if the product α.фe is calculated at any level of mortality on the population, then the mean recruitment at that level can be expressed as a percentage of the asymptote. 151 220.127.116.11 Steepness The steepness parameter h (Mace and Doonan 1988), also referred to as z (Myers et al. 1999), is the ratio of recruitment at 20% spawner abundance to the unfished recruitment. Since the parameter h represents the recruitment at a lower (20%) spawning stock biomass, it represents the curvature of the stock recruitment curve. For a Beverton and Holt SR curve, the h is analytically related to CR (Myers et al. 1999) as: h CR/(4 CR) Also for a Beverton and Holt SR curve, the value of steepness ranges from 0.2 to 1. A value of steepness equal to 0.2 corresponds to a compensation ratio equal to 1; which means recruitment is linearly related to spawning stock biomass (Beddington and Kirkwood 2005) and that there is no compensation. The other extreme value of h (1) denotes infinite compensation. Here it is assumed that for most species, the value of the steepness parameter h varies from 0.33 to 0.9. Please see Figure 6.1 for interpretation of BH-SR curves at different values of steepness. Of the estimates of steepness available for 56 species (Myers et al. 1999), only 6 species lay outside this range. A steepness value equal to 0.9 specifies that when the spawning stock biomass has decreased to 20%, the mean equilibrium recruitment is 90% of R0. Thus a steepness value of 0.9 denotes a high level of compensation. At h=0.9, the corresponding estimate for CR is 36. If it is assumed that for most of the species the range of steepness parameter is between 0.33 and 0.9, then the range within which the Beverton and Holt α parameter would vary for each species can be calculated by dividing the CR estimates at h=0.33 and h=0.9 by the estimates of фe0. (because as mentioned earlier CR is a product of Beverton-Holt α parameter and фe0). 152 Figure 6.1 Beverton-Holt stock recruitment curves at different values of steepness. At high levels of steepness the value of practical R0 is very close to the recruitment at asymptote R0.is the unfished recruitment and h (steepness) is the is the ratio of recruitment at 20% spawner abundance to the unfished recruitment. 18.104.22.168 Mean equilibrium recruitment for ~1800 species Unfished fecundity per recruit and alpha parameter (ф e0 ) Life history parameters (L∞, k, t0, parameters a and b of length-weight relationship, maximum age, age at maturity) needed for the estimation of фe0, and estimates of natural mortality are available for ~1800 species from the Fishbase database. The unfished fecundity per recruit, фe0, is calculated using equations described in the section 22.214.171.124. It is assumed that the natural mortality remains constant throughout the lifespan of the fish. The two assumed extremes within which the Beverton and Holt α parameter would vary for ~1800 species are calculated as: low 2 / e0 and high 36 / e0 . 153 Fished fecundity per recruit (ф e ) i. The fished fecundity per recruit, фe, is calculated across a range of total mortality (Z) values (0.05 to 4.6) using equations described earlier in section 126.96.36.199. A constant total mortality throughout the lifespan of the fish is assumed. Thus, similar to the calculation of B/R, in the calculation of фe, it is assumed that the fish have fully recruited to the fishery at age 1. ii. The product of Beverton-Holt α parameter and фe are calculated for low compensation and high compensation BH curves as low . e and high .e respectively. iii. The mean equilibrium recruitment (measured as a percent of the asymptote) at the low and high compensation levels are calculated as 100 (100 /(l ow.e )) and 100 (100 /(l ow.e )) respectively. 6.3 Results The results for biomass per recruit and mean recruitment are for the simplified situation when the age 1 fish is fully vulnerable to fishing. 6.3.1 Biomass per recruit The log of B/R shows a linear trend against total annual survival within a range of survival from 0.1 to 0.7 (Figure 6.2); outside this range of survival values, the relationship becomes curvilinear. The slope of the regression lines decreases exponentially with the increase of von Bertalanffy growth coefficient k. At high value of k (>2 yr-1), the slope reaches an asymptote (see Figure 6.3a). The high slope for slow growing species indicates that a change in survival has a considerable impact on the size of the population. For fast growing species, the change in B/R with change in survival is comparatively smaller. This indicates that fast growing species can tolerate wider ranges of mortality compared to slow growing fish. The k values used in the analysis range from 0.01 to 10 based on the range of k values in Fishbase (Freose and Pauly 2010). 154 Figure 6.2 Plot of log biomass per recruit against survival. Only the open dots were used in the regression. For the results shown here W ∞ is 1000g and maximum age of the fish is 10 years. The values adjacent to the lines are the values for growth parameter k. Figure 6.3 Slope and intercept of log biomass per recruit against growth coefficient k. Note k is on log scale in both the plots. 3a Plot of change in slope (log B/R against survival) with growth coefficient k. 3b Plot of change in intercept (log B/R against survival) with growth parameter k. The plot only shows the component governed by growth parameter. The value of the intercept is the sum of log (W∞) and the component shown in Figure 3b. 155 For the results seen in Figure 6.2, W∞ was fixed at 10,000 g. Changing W∞ only changes the intercept of the regression line and does not change the slope of the relationship between B/R and survival. Thus, the absolute change depends on the W∞, but the rate of change does not depend on W∞. The intercept is directly proportional to the sum of log(W∞) and a component that varies negatively with an exponent of k (Figure 6.3b). Changing the maximum age of the fish in the analysis affects the estimates of log B/R obtained at high levels of survival (see grey dots at high levels of survival in Figure 6.4a and 6.4b). High maximum age combined with high survival rates gives higher estimate of B/R. The reason for this observation is that at low rates of survival, the numbers surviving to high ages (>10) are so low that they do not cause a large change in the pattern; therefore, the changes become evident at high survival rates. For slow growing species, the slopes calculated in the section above increase slightly with increase in the maximum age of the fish. Figure 6.4 Plot of log biomass per recruit against survival at 2 levels of maximum age. Panel a, maximum age equals 10. Panel b, maximum age equals 60. Note the difference in the grey dots at high survival. 156 6.3.2 Recruitment The mean recruitment declines with increase in mortality; the decline is faster at low (0.33) value of steepness. When recruitment compensation is high (steepness is high), then the decline in mean recruitment is relatively slower. Curves can be drawn with percent recruitment on the vertical axis and the instantaneous total mortality on the horizontal axis (Figure 6.5). The grey lines in the figure show the mean recruitment for a species at low and high levels of compensation. The black line shows the equilibrium recruitment according to Myer‘s (1999) estimate of h. Such curves allow the comparison of recruitment pattern across species because on the vertical axis, recruitment is referred to as a percentage of the recruitment at the asymptote. Figure 6.5 Change in equilibrium mean recruitment with change in survival for 2 species. The light grey lines show the mean recruitment at steepness h=0.33 and the dark grey lines show the mean recruitment at steepness h=0.9. The black lines show the lines for mean recruitment based on steepness estimates for the species in Myers et al. (1999). Similar to the examples above, mean equilibrium recruitment curves at low and high compensation are obtained for ~1800 species. The results are grouped according to the growth parameters (k and W∞) for the purposes of presentation and for exploring any overarching patterns. Figure 6.6a shows the results for species with k <=0.3yr-1 (for example, Greenland halibut Reinhardtius hippoglossoide, Haddock Melanogrammus 157 aeglefinus, Red snapper Lutjanus campechanus, Striped bass Morone saxatilis) while Figure 6.6b shows the results for species with k>0.3 yr-1 (for example, Whiting Merlangius merlangus, Gulf menhaden Brevoortia patronus, Anchovy Engraulis encrasicolus). 158 (Z) 159 (Z) Figure 6.6 Mean recruitment curves for ~1800 species plotted against total mortality Z. The species are grouped by von Bertalanffy k (rows) and W ∞ (columns). The light grey lines show the calculated mean recruitment when h=0.33 and dark grey lines show the calculated mean recruitment when h=0.9. The black lines show the mean recruitment based on Myer’s estimate of h for some species within the range of k and W∞ in the respective panel . The number on top of each panel shows the number of species plotted. Figure 6a shows species with k<0.3. The first panel of Figure 6a, shows the only 9 species in FishBase database with k<0.05, the next 3 panels (across) show fish species with k in range 0.05 to 0.1. From the second row onwards, the range of k is same across the panel (shown on top of the 1st graph) and the range of W∞ is same downwards (shown on the top of columns in panel 2). Figure 6b shows species with k>0.3. 160 The results show that expectations of mean recruitment levels are influenced by the life history parameters. At very low values of k and W∞, even high levels of recruitment compensation do not offer a high tolerance to mortality. For such species (under the assumptions on selectivity in the analysis) recruitment rapidly decreases at very low levels of mortality (Z<0.5). With increase in k, the scope of compensation in recruitment increases. 6.4 Discussion 6.4.1 Selectivity Fishing gear selectivity is important for stock assessment and management (McClanahan and Mangi 2004; Walters and Martell 2004; Pitcher and Ainsworth 2010). In order to simplify the complications arising from combining the effects of selectivity and maturity, a simplistic situation in which age 1 fish are fully vulnerable to the fishery is modelled. With increase in the age at first capture, the B/R in the field would be higher for a given level of fishing mortality (to a certain extent before the natural mortality drives the exponential population decline). The calculation of fecundity per recruit ‗фe‘ also depends on the age at which fish recruit to the fishery. Thus the results discussed in the following sections pertain to the situation when age 1 fish is fully vulnerable to fishing. Species that recruit to the fishery much earlier than they mature are highly vulnerable; the ―limits of exploitation‖ are related to the difference between the age at maturity and the age at capture (Myers and Mertz 1998). A higher age at recruitment into the fishery would push the estimate of фe higher because greater numbers of individuals would reach the age at maturity. Thus if the fish population is selected to the fishery at a later age (>1) then the population would be able to tolerate higher values of fishing mortality. Therefore, the assumption regarding selectivity is of greatest consequence to the results for large fish that mature later and that are caught by a selective method of fishing. The assumption made here regarding selectivity would be realistic to some extent for tropical fisheries where the catches (of small fish) from several pelagic fisheries are directed to production of fish meal. Tropical demersal fisheries (mesh size of trawls less 161 than 2 cm) target shrimp for export resulting in bycatch and discards (Pauly et al. 1989). Similarly blast fishing on reefs is not selective for the size of fish. These problems in tropical fisheries are exacerbated by the open access and multi-species (a gear may catch mature fish of one species but immature fish of other species (Gobert 1994), multi-gear nature of the fisheries (one gear may catch mature fish but another gear may catch immature fish). Global scale of by-catch and discards of under-sized fish (Alverson 1994) show that the selectivity determined from landed catch may be different from the real extraction from the fish population. At high fishing pressure, when the older fish reduce in number, then fisheries tend to select smaller age groups within the population; this is known to have led to the collapse of several stocks (Myers and Mertz 1998). The discussion pertaining to restoration, in the following sections, relates to response of species responding to removal of fishing pressure from situations similar to the ones highlighted above. 6.4.2 Biomass per recruit 188.8.131.52 The importance of growth coefficient k The slope of change in log B/R depends only on the growth coefficient k. The implications of the results are that for slow growing species, the effect of decrease in mortality values (for example achieved through setting up of an MPA) would be a relatively steep increase in the B/R of the population. Alternatively for fast growing species, a smaller response (increase) in population size would be observed with decrease in mortality. Strategies for rebuilding should be, therefore, focused on slow growing species. Several studies discuss trophic cascades with respect to restoration dynamics in MPAs (Walters et al. 1999; Graham et al. 2003). The prey species are usually faster growing fish. The analysis shows that because the slope of change in log B/R with survival is less steep for fast growing fish, fast growing fish are relatively more stable against changes in mortality. For the same reason, the response to protection of fast growing fish would be weaker. The result is corroborated by the findings by Ault et al. (2005) who concluded, ―overfishing appeared most severe for long-lived, slow growing fish‖. Empirical studies of recovery in MPAs have also shown a greater magnitude in 162 response of slow growing fish (Claudet et al. 2006; Molloy et al. 2009). The results also lessen the concern about increased predation mortality inside MPAs, but this author suggests that expected increase in predation mortality on smaller species should be compared against the expected reductions in fishing mortality in the MPAs. The regressions also show the implications of change in growth. If under fishing pressure the growth rate of the species increases (i.e. k increases), then the species would climb upward on Figure 6.2. At the higher k, the slope of log B/R would be lower (exponentially) and would allow greater tolerance to mortality. If after the fishing pressure is released, the growth rate does not revert to the earlier lower level, then the B/R attained at higher survival rates would be lower than the B/R levels attained at the same survival rate had the k not changed. 184.108.40.206 The importance of Weight infinity and maximum age The intercepts of the regression of log B/R against survival are dependent on W∞. Therefore in a comparison of two species with equal k but different W∞, the species with higher W∞ will show a greater absolute increase in B/R. At any value of k, a higher W∞ signifies a higher B/R; this might imply a higher advantage against change in mortality. Fish species growing to older ages show an exponential increase in log B/R at survival rates higher than 80%. Higher maximum age is related to lower k and lower natural mortality rates. If it is expected that survival rates of more than 70% are possible in the no-take areas, then no-take areas will give a quicker and greater increase than allowing limited fishing mortality (effort reduction inside protected area) for a prolonged period. For all ranges of the k parameter, it is observed that at average annual survival rates lower than 20% (Z=2.7), the log B/R curve decreases rapidly compared to decrease in survival rate (under the assumption of selectivity in the analysis). If the fisheries in a region are non-selective in nature then at such mortality rates effort reductions or MPAs should be implemented to arrest the decline of the population biomass. 163 6.4.3 Recruitment 220.127.116.11 Implications of assumptions In this section, first the implications of the data used and the assumptions made in section 2.2 on recruitment are discussed. Before proceeding with the discussion, this author would like to stress that these assumptions have allowed a clear pattern to emerge. i. Estimates of M in Fishbase are not based on empirical relationships; however a few of the estimates of natural mortality have probably been calculated using empirical relationships (for example the Pauly (1980) formula which relates M to growth parameters k and L∞). Examples of such relationships have been reviewed in Vetter (1988). In such a case, any variations in natural mortality around the empirical regression relationships have been missed in this analysis. Estimates of M obtained from direct observations in the field could disperse the patterns observed here. ii. The фe0 is calculated as the sum over ages of survivorship and fecundity at age; the fecundity is calculated as the product of weight at age and maturity at age. Therefore, it is assumed that above the age at maturity, the fecundity is proportional to body weight. This assumption has been made in similar studies (Goodwin et al. 2006; Forrest et al. 2008) and the assumption increases the correlation between фe0 and W∞ for any species. Constant natural mortality is also a common assumption used for the calculations of фe0. iii. No uncertainty was allowed on the life-history or natural mortality parameters from Fishbase. Small changes in life history parameters (L∞, k, t0, parameters a and b of length-weight relationship, maximum age, age at maturity) can affect the weight at age used in the calculation of фe and bias the value upward or downward. The results show only the deterministic equilibrium. Here it is hoped that the uncertainty is accounted for by using a large number of fish species in the analysis. iv. The Beverton-Holt stock recruitment curve was used to model recruitment because the steepness and compensation ratio are analytically related by a 164 simple relationship, and the steepness ranges between the extremes 0.2 and 1. For species that follow overcompensatory (Ricker) relationships, the mean recruitment at high survival rates (lower levels of mortality) will be lower than the highest values for mean recruitment for the species. Complex patterns like depensation effects at low stock sizes have also been ignored (Liermann 2001). 18.104.22.168 Overarching patterns relating life history with recruitment Species with the lowest k and highest W∞ combination (large, slow growing species) are those which have the longest lifespans (50-100 years) and the lowest natural mortality rates (0.04 to 0.12). Compensation in recruitment allows the population to survive at total mortality levels almost double the natural mortality (0.1 to 0.25) on the population. When these species experience much high mortality rates, the фe declines rapidly, and the compensation offered by increased numbers of recruits at low population levels is small relative to the decline in фe. This is the reason why even with high compensation, for species with k<0.1 yr-1, the mean equilibrium recruitment declines much rapidly compared to species with higher k. It was assumed that fish were fully vulnerable to fishing from age 1. The result shows that if slow-growing late maturing species are vulnerable to fishing from age 1, then these species would decline at very low levels of fishing pressure. The фe declines rapidly because very few fish reach the age at the maturity. Therefore, it is for such species that the age at which the fish become vulnerable to fishing is of utmost importance. If the fish become vulnerable at a later age, then the decline in фe would be more gradual and the species will be more tolerant to higher fishing pressure. The results obtained here stress the influence selectivity has on the amount of fishing pressure which can be exerted on fish populations, especially longlived species like rockfish and orange roughy. The ratio between the estimate of BH α parameter at high compensation (h=0.9, CR=36) and at low compensation (h=0.33, CR=2) for any species in this analysis is always 18 (36/2) irrespective of whether the actual value for α is 10 or 1000 or higher. For example, at two levels of compensation CR = 2 and CR=36, species A has фe0=10, and species B 165 has фe0=2. For species A, the α at low and high compensation levels will vary between 0.2 and 3.6. For species B, the α at low and high compensation levels will vary between 1 and 18. Increase in W∞ within any given range of k corresponds to a longer lifespan and lower natural mortality, both factors that lead to a higher estimate of фe0. A higher estimate of фe0 leads to lower estimate of BH α parameter. The negative correlation between BH-SR α parameter and фe0 is also reported from analysis using real spawner recruit data (Denney et al. 2002; Goodwin et al. 2006) In the earlier section on growth, the results showed that the slope of change in B/R depended on k. Besides the information used in calculation of B/R—the weight at age and survivorship at age—the maturity schedule is only the other information required to calculate фe. Consequently, the shape of decline in фe also depends on k. Since the percentage change in mean recruitment is dependent on the product of BH-SR α parameter and фe, the shape of the decline in mean recruitment is highly influenced by the shape of decline of фe with mortality (the shape of фe as altered by the levels of compensation). In the example above, if species A and B have the same value for k, then the mean recruitment curves at low and high compensation will be similar. This is the reason why the shapes of the mean recruitment curves are very similar within the same range of k (across the panels in Figures 6.6a and 6.6b). It is to be noted that the results do not imply that species within the same range of k have the same recruitment pattern. The results show that if species within the same range of k have the same level of recruitment compensation, then the pattern of change in recruitment will be similar. In the section on biomass per recruit, the results showed that with increase in k, the rate of change in B/R with change in survival decreased (i.e. the slope decreased). Similarly, the rate of change in фe with change in survival decreases with increase in k. Therefore as growth rate increases, the фe spreads across a larger range of mortality values. For fast growing species (k >0.4 yr-1) depending on the level of compensation, recruitment curves spread over a wider range of total mortality from 0.5 to 4 or higher. Even at low levels of compensation (h=0.33), the decline in mean recruitment (with increase in mortality) for species in this category is gradual. Also, for the fast growing species, a small increase in 166 compensation can cause a huge difference in the shape of mean recruitment curve. The response of biomass per recruit and mean recruitment against change in mortality are steeper for slow growing fish, therefore, the magnitude of decline and recovery would be larger. The results from recruitment also suggest a higher (in terms of magnitude) response from protection of slow growing species, the result corresponds with empirical findings (Claudet et al. 2006; Molloy et al. 2009). 22.214.171.124 Compensation Ratio The фe0 is correlated with body size (Goodwin et al. 2006) because it is derived from growth parameters. In Figures 6.6a and 6.6b, the highest estimates for фe0 are for the species in the top right panel because these species have the highest range for W∞ and the longest lifespans. The estimate of фe0 decreases from right to left with decrease in W∞. Based on a comparison of 54 stocks, Goodwin et al. (2006) found that stocks with high фe0 (―large bodied late maturing‖) had high CR and concluded that фe0 ―proved to be the best single predictor of both α and CR‖. A meta-analysis of ~200 North American freshwater and marine species (Rose et al. 2001) categorized fish species as ―periodic – large highly fecund fish with long life spans‖, ―opportunistic – small rapidly maturing short lived species‖, and ―equilibrium – intermediate size producing relatively large offspring and showing parental care‖ (Rose et al. 2001
UBC Theses and Dissertations
Marine ecosystem restoration with a focus on coral reef ecosystems Varkey, Divya Alice 2010
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