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Ecological and economic analyses of marine ecosystems in the Bird's Head Seascape, Papua, Indonesia:.. Bailey, Megan; Pitcher, Tony J. 2008

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ISSN 1198-6727  Fisheries Centre Research Reports 2008 Volume 16 Number 1  Ecological and Economic Analyses of Marine Ecosystems in The Bird’s Head Seascape, Papua, Indonesia: II  Fisheries Centre, University of British Columbia, Canada  ECOLOGICAL AND ECONOMIC ANALYSES OF MARINE ECOSYSTEMS IN THE BIRD’S HEAD SEASCAPE, PAPUA, INDONESIA: II  edited by Megan Bailey and Tony J. Pitcher  Fisheries Centre Research Reports 16(1) 186 pages © published 2008 by The Fisheries Centre, University of British Columbia, 2022 Main Mall Vancouver, B.C., Canada, V6T 1Z4  ISSN 1198-6727  F  ISHERIES  C  ENTRE  R  ESEARCH  R  EPORTS  V  OLUME  16 N  UMBER  1 2008  ECOLOGICAL AND ECONOMIC ANALYSES OF MARINE ECOSYSTEMS IN THE BIRD’S HEAD SEASCAPE, PAPUA, INDONESIA : II edited by Megan Bailey and Tony J. Pitcher  CONTENTS  Page  DIRECTOR’S FOREWORD .................................................................................................................................1 EXECUTIVE SUMMARY ................................................................................................................................ 2  CHAPTER 1. ECOSYSTEM SIMULATION MODELS OF RAJA AMPAT, INDONESIA, IN SUPPORT OF ECOSYSTEMBASED FISHERIES MANAGEMENT  Cameron H. Ainsworth, Divya A. Varkey and Tony J. Pitcher Abstract ............................................................................................................................. .. 3 Introduction ............................................................................................................................. .. 4 Overview ............................................................................................................................. . 4 The Raja Ampat Islands .......................................................................................................... .. 4 The Physical System................................................................................................................. .. 4 Fisheries ............................................................................................................................. .. 4 A New EBFM Initiative .......................................................................................................... .. 5 Trophodynamic Modelling ..................................................................................................... .. 5 BHS EBM project data integration ......................................................................................... . 5 Materials And Methods ............................................................................................................................. .. 7 Ecopath With Ecosim .............................................................................................................. .. 7 BHS EBM Project Data Integration.............................................................................................................. .. 8 Reef Health Monitoring .......................................................................................................... .. 8 Area Ratio Conversions ........................................................................................................... 10 Catch And Fishing Effort Parameter Revision ........................................................................ .12 Fisher Interviews ..................................................................................................................... 20 Gut Content Analysis .............................................................................................................. 23 Ecopath Balancing .................................................................................................................. 26 Present-Day Raja Ampat Model ............................................................................................. 26 Raja Ampat Model For 1990 ................................................................................................... 28 Sub-Area Models ..................................................................................................................... 29 Ecosim Tuning .......................................................................................................................... 30 Vulnerability Parameterization ............................................................................................... 30 Mediation Functions................................................................................................................ 30 Primary Production Forcing ................................................................................................... 32 Time Series Reconstruction..................................................................................................... 33 Equilibrium Analysis ............................................................................................................... 34 Results ............................................................................................................................. 35 Reconstructed Historical Biomass From LEK Data ............................................................... 35 Reef Health Monitoring ......................................................................................................... 35 Herbivorous Fish ..................................................................................................................... 36 Piscivorous Fish ...................................................................................................................... 36 Gut Content Analysis .............................................................................................................. 39 Stomach Sample Results ........................................................................................................ 39 Ecosim Analysis ...................................................................................................................... 42 Equilibrium Analysis .............................................................................................................. 42  Discussion  ...............................................................................................................................44 Fish Biomass ............................................................................................................................44 Requested EBFM Analyses Using Ewe Models .......................................................................44 Future Work .............................................................................................................................45 Conclusions ...............................................................................................................................46 Exploitation Status Of Raja Ampat Reef Fisheries .................................................................46 BHS EBM Project ....................................................................................................................46 Acknowledgements ...............................................................................................................................47 References Cited ...............................................................................................................................47 Appendix A - Biomass Calculations .............................................................................................................53 A1 Reef Health Monitoring .......................................................................................................53 A2 Area Calculations ...............................................................................................................58 Appendix B - Interview Data......................................................................................................................... 60 B1 Fisher Responses ................................................................................................................. 60 B2 Analysis Of Lek Data ...........................................................................................................64 Appendix C - Gut Content Analysis ...............................................................................................................69 C1 Stomach Sample Methods ...................................................................................................69 C2 Stomach Sample Results .....................................................................................................70 Appendix D - Ewe Parameterization.............................................................................................................. 75 D1 Biomass ...............................................................................................................................74 D2 Catch ...............................................................................................................................78 D3 Trophic Interaction Matrices .............................................................................................83 Appendix E – Ewe Model Output ................................................................................................................117 E1 Time Series .........................................................................................................................117 E2 Equilibrium Analysis ..........................................................................................................121 Appendix F –Abstract Of Manuscripts Submitted And In Press ................................................................123  CHAPTER 2. TOWARDS ECOSYSTEM-BASED MANAGEMENT IN THE BIRD’S HEAD FUNCTIONAL SEASCAPE OF PAPUA, INDONESIA: THE ECONOMIC SUB-PROJECT  Megan Bailey and U. Rashid Sumaila Abstract ..............................................................................................................................125 Introduction ..............................................................................................................................125 Objectives ..............................................................................................................................126 Objective 1: Intergenerational discounting..............................................................................126 Objective 2: Development options ..........................................................................................126 Methods ..............................................................................................................................127 Intergenerational discounting .................................................................................................127 Development options ..............................................................................................................128 Results ..............................................................................................................................128 Intergenerational discounting .................................................................................................128 Mining case study ....................................................................................................................129 Development options ..............................................................................................................130 Fishing ..............................................................................................................................130 Mining ..............................................................................................................................132 Tourism and MPAs...................................................................................................................134 Logging ..............................................................................................................................137 Pearl farming ...........................................................................................................................138 Health and demographics ...........................................................................................................................139 Discussion And Conclusion...........................................................................................................................139 Intergenerational discounting ................................................................................................139 Development options ..............................................................................................................140 References Cited ..............................................................................................................................140  CHAPTER 3. DESTRUCTIVE FISHING IN RAJA AMPAT, INDONESIA: AN APPLIED PRINCIPALAGENT ANALYSIS  Megan Bailey and U. Rashid Sumaila Abstract ............................................................................................................................. 142 Introduction ............................................................................................................................. 142 IUU fishing ............................................................................................................................. 142 Principal-agent theory ........................................................................................................... 143 Discounting ............................................................................................................................. 143 Model outline .......................................................................................................................... 143 Artisanal Fisheries .................................................................................................................. 143 Players ............................................................................................................................. 144 Methods ............................................................................................................................. 145 Biological model ..................................................................................................................... 145 Population dynamics without fishing ..................................................................................... 145 Population dynamics with fishing ......................................................................................... 145 Fishing strategies .................................................................................................................... 145 Economic Model ..................................................................................................................... 146 Revenue ............................................................................................................................. 146 Cost ............................................................................................................................. 146 Net benefit ............................................................................................................................. 147 Optimization ............................................................................................................................ 147 Lagrangian function ............................................................................................................... 148 Solution algorithm 1................................................................................................................. 149 Data ............................................................................................................................. 150 Snapper fishery biological data ............................................................................................... 150 Snapper fishery economic data................................................................................................ 151 Grouper fishery biological data ............................................................................................... 151 Grouper fishery economic data ............................................................................................... 152 Results ............................................................................................................................. 152 Snapper model results ............................................................................................................ 152 Exploring the value of the relative impact of illegal fishing ................................................... 152 Baseline solution ..................................................................................................................... 154 Optimal solution ...................................................................................................................... 155 Discounting ............................................................................................................................. 158 Sensitivity analysis .................................................................................................................. 158 Grouper model results ............................................................................................................ 160 Exploring the value of the relative impact of illegal fishing ................................................... 160 Baseline simulation ................................................................................................................. 161 Optimal solution ...................................................................................................................... 162 Discounting ............................................................................................................................. 163 Sensitivity analysis .................................................................................................................. 163 Discussion ............................................................................................................................. 164 Blast fishing for snapper .......................................................................................................... 164 Cyanide fishing for grouper ..................................................................................................... 164 Conclusion ............................................................................................................................. 165 Acknowledgments ............................................................................................................................. 165 References cited ............................................................................................................................. 166  CHAPTER 4. ECOSYSTEM-BASED MANAGEMENT: THE INFLUENCE OF A PROJECT IN RAJA AMPAT, PAPUA, INDONESIA  Divya A. Varkey, Tony J. Pitcher, and Cameron H. Ainsworth Abstract ............................................................................................................................. 169 Introduction ............................................................................................................................. 169 Method ............................................................................................................................ 170 Results and discussion ............................................................................................................................. 172 References ............................................................................................................................. 173 Appendix ............................................................................................................................. 175  CHAPTER 5. ECOLOCATOR USER’S GUIDE  Cameron H. Ainsworth Introduction ..............................................................................................................................176 Program operation ..............................................................................................................................176 Inputting biomass dynamics into EcoLocator.........................................................................176 Application to Ecospace ..........................................................................................................177 Creating or loading an EcoLocator map file ..........................................................................177 Virtual boundary ......................................................................................................................178 Biomass distribution panel ......................................................................................................179 Land behaviour controls ..........................................................................................................181 Biomass calculations ................................................................................................................181 Output ..............................................................................................................................182 Support, liability and copyright ...................................................................................................................183 References ..............................................................................................................................183  Suggested Citation: Bailey, M. and Pitcher, T.J. (eds) (2008) Ecological and Economic Analyses of The Bird’s Head Seascape, Papua, Indonesia: II. Fisheries Centre Research Reports 16(1): 186 pp. A Research Report from Fisheries Ecosystem Restoration Research & the Fisheries Economics Research Unit Fisheries Centre Research Reports 16(1) 186 pages © Fisheries Centre, University of British Columbia, 2008  F ISHERIES C ENTRE R ESEARCH R EPORTS ARE A BSTRACTED IN THE FAO A QUATIC S CIENCES AND F ISHERIES A BSTRACTS (ASFA) ISSN 1198-6727  Fisheries Centre Research Reports are funded in part by grant funds from the Province of British Columbia Ministry of Environment.  1  DIRECTOR’S FOREWORD This report being a sequel to ‘Ecological and Economic Analyses of Marine Ecosystems in the Bird’s Head Seascape, Papua, Indonesia I’ (2007), the first question we should ask is what the original report was, in terms of sequence. We do know that it was not a prequel: this clearly was ‘Historical Ecology of the Raja Ampat Archipelago, Papua Province, Indonesia’, i.e., FCRR 14(7), published in 2006. Thus, a new name (technically a retronym) is needed for the original which spawns the sequel(s). My suggestion is ‘urquel’, from ‘ur’, i.e., ‘first’ or ‘original’ in German, and Quell(e), a source, also in German, which should satisfy philologists, Germanophiles and cerevisaphiles. Now to the sequel. The nice thing about models is once you have them they become attractors for more and better data. This is the case here: an ecosystem model has been generated for the marine part of Raja Ampat, in spite of this being one of the most remote regions of the world, as elaborated upon in the …urquel. Now, having this model, it has become possible for the authors of the four papers in this report to generate another round of hypotheses that they can test and scenarios that they can run – again: all of this in an area that a few years ago was supposed to be devoid of data and not amenable to study using ecosystem modeling. This is fantastic. I am also pleased that the questions and scenarios that are run are not exclusively biological ones, i.e., ‘what would happen to predators and prey if species x were fished more heavily’. Rather economic questions are being posed of the management implications of various scenarios, which are obviously the ones that will appeal the most to policy-makers.  2  Chapter 1 Ecosystem Simulation Models of Raja Ampat  EXECUTIVE SUMMARY In an era of declining fish stocks and habitat degradation, ecosystem-based management (EBM) is considered an alternative approach to promote the sustainability of marine resource use. The regency government in Raja Ampat, Papua, Indonesia, is considering implementing an EBM approach to marine management in their area. Raja Ampat is part of the Bird’s Head Functional Seascape (BHS), an area of high marine biodiversity. Under a grant from the David and Lucile Packard Foundation, Conservation International (CI), The Nature Conservancy (TNC), World Wildlife Fund (WWF), the State University of Papua (UNIPA) and researchers from the Fisheries Centre at the University of British Columbia (UBC) have come together to assist in the EBM initiative for the BHS, and specifically for Raja Ampat. This research report presents materials prepared by the Fisheries Ecosystems Restoration Research group (FERR) and the Fisheries Economics Research Unit (FERU) at the Fisheries Centre at UBC, and is the third research report to be published from this project1. The first paper highlights new ecosystem-based modelling developed by the FERR group using Ecopath with Ecosim (EwE) software, a quantitative tool used to simulate ecosystem interations. A preliminary working model was published in an earlier research report (see footnote below), but several improvements are documented here, including local data from stomach sampling and reef health monitoring surveys, as well as a new diet algorithm and estimates of the total Raja Ampat fish catch including illegal, unreported and unregulated components (IUU). A new departure in this project has been the discussion, acquisition and use of extensive local data required by the modellers from field sampling and interviews carried out by the field teams. The final EwE model, tuned to this local data, has been used to examine a series of realistic EBM policy scenarios suggested by the project partners and stakeholders in Indonesia. As part of the quantitative management advice that may underpin the implementation of EBM in Raja Ampat, the detailed results are presented here and in six manuscripts that have been accepted, submitted, or are in preparation for peer-reviewed journals (see Appendix E). The second paper in this report presents two small studies undertaken by FERU members as part of the BHS economic sub-project. The first study compares the value of Raja Ampat’s main economic sectors through time by applying two different methods of discounting: conventional and intergenerational. This simple analysis demonstrates that under conventional discounting, management choices favouring marine conservation may not seem cost-effective due to short-term costs. The second study in this paper presents the outcomes of a discussion on possible development options in Raja Ampat. The interview was intended to highlight the interaction among different economic sectors in Raja Ampat, in hopes of eventually contributing to a quantitative model linking sectors. The third paper in this report presents a game-theoretic model supporting the implementation of EBM by examining the possible incentives that could be used to shift fisher effort away from destructive fishing gears in Raja Ampat. There is virtually no monitoring and enforcement currently in place in Raja Ampat, and fishers worry that the use of cyanide and explosives to catch grouper and snapper may be negatively affecting reef health and fish populations. The elimination of blast fishing could bring economic benefit to the area, but the high profitability of the cyanide fishery appears to be a barrier to economic benefits through the elimination of the gear. The fourth section of this report evaluates the expected progress from the successful implementation of the BHS EBM project. The authors contend that a considerable improvement in management might be expected in Raja Ampat as a result of the EBM work. The final contribution in this report describes a new modelling tool called EcoLocator for use with EwE that displays the biomass distribution of species at highly spatial resolution. EcoLocator was developed specifically for the Bird’s Head EBM project, but is generalized for use with any EwE model.  Megan Bailey and Tony Pitcher Vancouver, Canada, May 2008  1 The first two contributions were: Palomares, M.L.D., Heymans, J.J., 2006. Historical Ecology of the Raja Ampat Archipelago, Papua Province, Indonesia. Fisheries Centre Research Reports 14(7): 64 pp., and Pitcher, T.J., Ainsworth, C.A., Bailey, M. (Eds.), 2007. Ecological and Economic Analyses of Marine Ecosystems in the Bird’s Head Seascape, Papua, Indonesia: I. Fisheries Centre Research Reports 15(5): 184 pp.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  3  CHAPTER 1 ECOSYSTEM SIMULATION MODELS OF RAJA AMPAT, INDONESIA, IN SUPPORT OF ECOSYSTEM-BASED FISHERIES MANAGEMENT1 Cameron H. Ainsworth, Divya A. Varkey, and Tony J. Pitcher Fisheries Ecosystems Restoration Research, Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver, BC, Canada, V6T 1Z4  ABSTRACT This report describes synoptic ecosystem models employing the Ecopath with Ecosim (EwE) framework for the Raja Ampat archipelago in Eastern Indonesia and we provide examples of their use in support of ecosystem-based fisheries management (EBFM). This is the final technical report for the Bird’s Head Seascape Ecosystem-Based Management project (BHS EBM) from the Fisheries Ecosystems Restoration Research (FERR) group at the UBC Fisheries Centre. The project is a David and Lucile Packard Foundation-funded initiative jointly among The Nature Conservancy (TNC), Conservation International (CI), World Wildlife Fund (WWF) and other partners in Indonesia, with two UBC Fisheries Centre teams providing modelling (FERR) and economics (FERU) support since its inception in 2005. This document supports a number of peer-reviewed publications, in press, submitted and in preparation, which answer specific EBFM research questions posed by scientific partners in Indonesia. By integrating project data gathered in the field by CI and TNC, we improve on the preliminary models described in 2007 in earlier reports. Locally-gathered information has been used to tune model parameters: this includes present biomass (from dive transects), fisheries (from overflight data), fishery catches (from resource surveys), fish diets (gut content analysis), local ecological knowledge about fisheries and habitats from interviews of artisanal and commercial fishing operators (from participatory rural appraisal and resource use surveys). In two cases (fish diets and fishers’ perceptions of biomass change) field surveys requested by the modelling group have been successfully carried out by TNC/CI teams; this is probably the first time in the world that those constructing ecosystem models have had the opportunity to interact with field teams in this way. We present the methodology used to process this field data into a form useable by the EwE models, and we present the parameterization and dynamic functioning of the models in a form for review. Model description in this report includes the balancing of the static Ecopath model, and tuning of the dynamic Ecosim model to time series catch estimates utilizing a novel assessment of illegal and unreported catch (previously reported), relative biomass estimates from fisheries data, and primary production trends. We also present an equilibrium analysis to demonstrate the current exploitation status of stocks in Raja Ampat. The full project team provided a final check on the models and identified EBFM scenarios for investigation at a workshop in Bali, June 2007. EBFM scenarios investigated with the models at the request of the project team are the likely ecosystem effects of: changes in the anchovy fishery after complete cessation, under limited quota, and under increased fishing; restricting the commercial exploitation of groupers; excluding all net fisheries for reef fish; blast fishing increased or kept at the status quo; increases in the tuna fishery; and Hawksbill turtles restored to former abundance. We also examine how an increase in fishing may affect the local ecology and economy, and attempt to examine what the unexploited ecosystem of Raja Ampat might have looked like. We conclude with a summary of forthcoming peer-reviewed articles, and suggestions for future ecosystem research in support of EBFM in the Bird’s Head Seascape.  1  Cite as: Ainsworth, C.H., Varkey, D.A., and Pitcher, T.J., 2008 Ecosystem simulation models of Raja Ampat, Indonesia, in support of ecosystem-based fisheries management. Pages 3-124 in Bailey, M., Pitcher, T.J. (Eds.) Ecological and Economic Analyses of Marine Ecosystems in the Bird’s Head Seascape, Papua, Indonesia: II. Fisheries Centre Research Reports 16(1): 186 pp.  4  Chapter 1 Ecosystem Simulation Models of Raja Ampat  INTRODUCTION OVERVIEW This is the final technical report prepared for the Bird’s Head Seascape Ecosystem-Based Management (BHS EBM) project by the Fisheries and Ecosystems Restoration Research (FERR) group at the Fisheries Centre, University of British Columbia. This document builds on the first technical report (Ainsworth et al. 2007), which provided preliminary Ecosim models for Raja Ampat and demonstrations of model behaviour. Here we finalize five EwE and Ecospace models of the Raja Ampat archipelago in Papua, Indonesia including present-day models for Kofiau Island, SE Misool Island and the Dampier Strait, together with models for Raja Ampat in 1990 and 2005. The models have been revised to include additional data and findings emerging from field studies in the BHS EBM project by partners in Indonesia (The Nature Conservancy (TNC), Conservation International (CI) and World Wildlife Fund (WWF)). Here, we present the methods used to process field data into a form usable by the models, and we present the final models’ parameterization and dynamic functioning in a form for review. This technical report will support other peer-reviewed contributions investigating questions of importance to ecosystem-based fisheries management (EBFM) in Raja Ampat and other coral reef ecosystems of the world.  THE RAJA AMPAT ISLANDS The physical system The Raja Ampat (RA) archipelago extends over 45,000 km2 and consists of approximately 610 islands including the ‘four kings’, Batanta, Misool, Salawati and Waigeo (COREMAP, 2005). Erdmann and Pet (2002) provide an overview of the major oceanographic features occurring in the Raja Ampat archipelago, while Firman and Azhar (2006) provide a detailed description of the geology, physical oceanography, coastal biology and resource use patterns in Raja Ampat (including mining, forestry and fishing sectors). Aerial photographs produced by the BHS EBM project are available online at www.rajaampat.org or on DVD2 . The area encompasses a variety of marine habitats, including some of the most biodiverse coral reef areas on Earth (Donnelly et al., 2003; McKenna et al., 2002a). It is estimated that RA possesses over 75% of the world’s known coral species (Halim and Mous, 2006).  Fisheries Skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares) and Spanish mackerel (Scomberomorus commerson) are pursued in commercial pole and line, trolling and purse seine fisheries. These constitute the majority of commercial catch, but export fisheries exist for high value reef fish products like groupers, snappers and Napoleon wrasse. Indonesia is known to have suffered a rapid depletion in recent decades of near-shore fish stocks and coral reef animals, especially sharks, turtles, tunas and reef-associated fish (Tomascik et al., 1997). Some of the depletions can be attributed to the sharp increases in price received for export products between 2000 and 2002, which was the result of the Asian economic crisis and the consequent strengthening of foreign currencies, particularly the US dollar, with respect to the Indonesian Rupiah3. By assembling available fisheries statistics, Ainsworth et al. (2007) were able to confirm that there has been a marked decline over the last 15 years in the catch-per-unit-effort (CPUE) in many targeted stocks. Despite the well-known inadequacies of CPUE data as an index of relative abundance (e.g., Beverton and Holt, 1957; Gulland, 1974; Hilborn and Walters, 1992), the especially sharp decline since 1990 allows us to make two conclusions regarding the current status of exploited marine resources in Raja Ampat. The first conclusion is that some targeted stocks have likely declined; the second conclusion is that stocks were, until at least the 1990s, in a very lightly exploited state. Only a lightly exploited ecosystem would be capable of such a drastic reduction in the catch rate, in the neighbourhood of an order of magnitude since 1990 for many species, barring any massive increase in fishing effort over that period.  2 3  Contact: Joanne Wilson, TNC CTC. Jl Pengembak 2, Sanur, Bali, Indonesia joanne_wilson@tnc.org. Christovel Rotinsulu. CI. Jl.Gunung Arfak.45. Sorong, Papua, Indonesia, personal communication.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  5  A new EBFM initiative Challenges to management of coral reefs in Raja Ampat, and elsewhere, now centre on the serious issues of overexploitation (Pandolfi et al., 2003), destructive fishing practices like cyanide fishing and blast fishing (Erdmann and Pet-Soede, 1996; Pet-Soede and Erdmann, 1998), land-based pollution (McCulloch et al., 2003; Kaczmarsky et al., 2005), climate change (Hughes et al., 2003; Harvell et al., 2002) and outbreaks of corallivores such as the crown of thorns starfish (Acanthaster planci), a frequent source of mass mortality in corals (Chesher, 1969). However, the Raja Ampat Regency government has shown initiative to protect the marine environment and serve as many as 24,000 commercial and artisanal fishers who rely on it (Dohar and Anggraeni, 2007). For example, a decree by the Bupati (Regent) in 2003 declared Raja Ampat to be a Maritime Regency and helped to establish a new network of marine reserves in 2006 covering more than 650,000 hectares of sea area and 44% of reef area. The fisheries office (Departemen Kelautan dan Perikanan, DKP) has further pledged to declare as much as 30% of the marine area protected in the Regency, exceeding the national goal of 20%4 . To facilitate the adoption of an EBFM strategy, The Nature Conservancy (TNC), Conservation International (CI), World Wildlife Fund (WWF) and the Fisheries Centre at the University of British Columbia (UBC) entered into comprehensive program of study with the goals of increasing the body of scientific knowledge in Raja Ampat and providing scientific advice to management bodies. The project is rare in that elements of the field sampling have been designed especially to support the ecosystem modelling, and there has been a strong, continuous interaction between modellers and field researchers. The contribution made in this report provides a foundation for the continued analysis of trophic dynamics in Raja Ampat through a dynamic model. The models have been constructed to be applicable to a variety of research questions, and can be readily updated as new information becomes available.  TROPHODYNAMIC MODELLING The trophodynamic (food web) Ecopath with Ecosim (EwE) models used in this study are constructed at various spatial scales to investigate scientific research questions of interest to EBFM, and especially to answer specific questions fielded by investigators in the BHS EBM project, the Raja Ampat Regency fisheries bureau, and other knowledgeable academics. The Raja Ampat model encompasses the greatest area, and includes the ‘four kings’ (the islands Batanta, Misool, Salawati and Waigeo) as well as shelf and oceanic in-flow areas. It accounts for only a fraction of the total area for the Bird’s Head Seascape but we chose to focus our attention on this area because most of the scientific studies conducted for the BHS EBM project are centred here, with the main exception of the turtle nesting habitat study conducted by WWF in Cenderwasih Bay and nearby regions. Also, we assume that the data repositories in Sorong will reflect the recorded fishery activities of the immediate Raja Ampat area most strongly, even though many of the data we received are aggregated by area. Finally, the expertise held by project members and scientific partners relates most strongly to the area of Raja Ampat. Scientific questions regarding the interrelation of functional groups and fisheries are investigated using the large Raja Ampat model in order to obtain a synoptic view of ecosystem functioning and the impact of management scenarios on industry, while models of smaller areas comprising Kofiau Island, Southeast Misool Island and the Dampier Strait (hereafter called the sub-area models) are used to investigate particular research questions requiring a degree of spatial resolution or site specificity. The sub-area models allow us to make useful predictions with respect to the outcomes of spatial management options, not limited to the placement and configuration of no-take areas and fishery restricted zones such as in marine protected areas (MPAs).  BHS EBM PROJECT DATA INTEGRATION Much of the data integrated here into the EwE models comes from the diverse studies in the BHS EBM project. These include reef health monitoring biomass and coral coverage estimates from SCUBA and 4 Becky Rahawarin. communication.  Kepala Dinas Perikanan dan Kelautan, Raja Ampat. Jl. A. Yani, Kuda laut, Sorong, Papua, personal  6  Chapter 1 Ecosystem Simulation Models of Raja Ampat  snorkelling diving transects, local ecological knowledge and fisheries knowledge from interviews of artisanal and commercial fishing operators, fisheries statistics and other data from various literature sources collected by project members and UBC researchers (Table 1.1.). The methods section provides a more detailed record of BHS EBM data sources and data processing methodology. Additional sources of information from published studies and databases provided a foundation for the models in Ainsworth et al. (2007). BHS EBM project outputs that could not be integrated into the models are discussed along with options for future study in the discussion section. Table 1.1 BHS EBM project data used in the EwE models. BHS EBM Output Quantitative data  By  Use  Reference  Reef health monitoring  TNC, CI  Ecopath parameterization; biomass and coral coverage (all models)  Local ecological knowledge interviews  CI  Bzero calculations for Ecosim. Article in preparation (shifting baselines), Time series biomass for Ecosim  Kofiau results: (A. Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Email: amuljadi@tnc.org. Unpublished data) SE Misool results: (M. Syakir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Email: msyakir@tnc.org. Unpublished data) Waigeo results (not used) (M. Erdi Lazuardi. CI. Jl Arfak No. 45. Sorong, Papua, Indonesia 98413. Email: erdi@conservation.or.id. Unpublished data) Protocol: Mous and Muljadi (2005); Pratomo and Setiawan (2006). Data collection: C. Rotinsulu. CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia. Email: chris@conservation.or.id. Unpublished data.  Resource use survey  TNC  Fish gut content analysis  CI  Coastal rural appraisal  TNC, CI  Participatory rural appraisal  TNC, CI  Socioeconomic valuation report  CI  Socioeconomic evaluation of anchovy fishery Coastal rural appraisal 2004  UBC TNC  Illegal, unreported and unregulated (IUU) catch (Varkey et al., in prep.) Catch estimation for Raja Ampat Ecopath diet matrix  Catch data used in Ecopath catch matrix. Fishing areas used to set Ecospace habitat types. Human population density to set sub-area model catch ratios Ecopath commodity price and fishing cost matrices. Ecopath catch matrix Ecopath catch matrix; Ecospace habitats; human population density to set sub-area model catch ratios  Data processing using fuzzy logic: Ainsworth et al. (2008) Protocol: Ainsworth et al. (2007; Appendix C1) A. Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Email: amuljadi@tnc.org. Unpublished data. This report. Data collection: C. Rotinsulu. CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia. Email: chris@conservation.or.id. Unpublished data. Data processing: This manuscript. Protocol: Ainsworth et al. (2007; Appendix C2) J. Wilson. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228. Email: joanne_wilson@tnc.org  C. Rotinsulu. CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia. Email: chris@conservation.or.id. Unpublished data. Dohar and Anggraeni (2007) Bailey et al. (2008) Anon. (2004); Protocol: Mous (2005)  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  7  Table 1.1 cont. Qualitative data MARXAN analysis  TNC  MPA testing locations for Ecospace  M. Barmawi. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228. Unpublished data. Contact: joanne_wilson@tnc.org. Barmawi, 2006.; Online interactive map: www.rajaampat.org; DVD photographs (M. Barmawi. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228. Unpublished data. Contact: joanne_wilson@tnc.org). Firman and Azhar (2006)  Aerial photography  TNC  Ecospace habitat  CI Resource atlas for the Regency of Raja Ampat Perception monitoring  CI  Ecospace habitat  TNC  Supporting literature  Historical ecology Previous work  UBC  Supporting literature  Halim and Mous (2006) Protocol: Halim et al. (2005) Palomares and Heymans (2006)  REA report 2002 Rapid Assessment 2002  TNC CI  Ecopath biomass Ecopath biomass  Donnelly et al. (2003) McKenna et al. (2002)  MATERIALS AND METHODS ECOPATH WITH ECOSIM To understand the impact that fisheries have in the coral reef ecosystem of Raja Ampat, we have used the Ecopath with Ecosim (EwE) suite of modelling tools. Although ecosystem models such as EwE offer no panacea, they can provide a new perspective on stock dynamics and can be used to explore unintuitive interactions that may have strong effects on the functioning and resilience of the ecosystem. EwE can be used to examine predator-prey feeding interactions, foraging behaviour, several types of fisheries impacts and abiotic effects such as climate. Although EwE models have been made for areas all over the world (Christensen and Walters, 2005), the models may be a most useful tool to EBM in a highly interconnected marine ecosystem such as a coral reef, where complex trophic interactions can be expected to have a significant and compounding effect on stocks. In fact, Ecopath was invented first to represent a coral reef ecosystem (Polovina, 1984). The presence of mixed fisheries in coral reef ecosystems also makes it impossible to manage stocks effectively as discrete entities, but managing stocks on the basis of multispecies functional groups (i.e., groups of species with similar life history characteristics and trophic niches) is a suitable alternative. Ecopath (Polovina 1984, Christensen and Pauly 1992) operates like a thermodynamic accounting system. It tracks the biomass or energy flow rates in and out of functional groups for one instant in time as instantaneous fluxes. Mass balance is maintained in functional groups according to Equation 2.1. n  Bi ⋅ (P B )i = Yi + ∑ B j ⋅ (Q B ) j ⋅ DC ij + E i + BAi + Bi (P B )i ⋅ (1 − EE i )  (2.1)  j =1  where, Bi and Bj are biomasses of prey (i) and predator (j), respectively; P/Bi is the production/biomass ratio; Yi is the total fishery catch rate of group (i); Q/Bj is the consumption/biomass ratio; DCij is the fraction of prey (i) in the average diet of predator (j); Ei is the net migration rate (emigration – immigration); BAi is the biomass accumulation rate for group (i); EEi is the ecotrophic efficiency; the fraction of group mortality explained in the model, while consumption (Q) for a predator group is calculated as in Eq. 2.2.  B ⋅ (Q B ) = B ⋅ (P / B ) + (1 − GS ) ⋅ Q − (1 − TM ) ⋅ P + B(Q B ) ⋅ GS  (2.2)  8  Chapter 1 Ecosystem Simulation Models of Raja Ampat  where GS is the proportion of food unassimilated; and TM is the trophic mode expressing the degree of heterotrophy (0 and 1 represent autotrophs and heterotrophs, respectively and intermediate values represent facultative consumers). Ecopath solves a set of n simultaneous linear equations of the form in Eq. 2.1, where n represents the number of functional groups in the model. The program therefore serves as a framework on which to place piecemeal information about the ecosystem and judge the compatibility of the available biological information under the constraints imposed by the thermodynamic requirements of both predator and prey. Through the assumption of mass balance, we can infer the unknown properties of the ecosystem based on the available data, which is extremely helpful in a data limited study area like Eastern Indonesia. Ecosim (Walters et al. 1997) adds temporal dynamics, describing the biomass or energy flux between compartments through coupled differential equations derived from Eq. 2.1. The set of differential equations is solved using the Adams-Bashford integration technique. Biomass dynamics are described by Eq. 2.3. n n dBi = g i ∑ f (B j , Bi ) − ∑ f (Bi , B j ) + I i − (M i + Fi + ei ) ⋅ Bi dt j =1 j =1  (2.3)  Where, dBi/dt represents biomass growth rate of group (i) during the interval dt; gi represents the net growth efficiency (production/consumption ratio); Ii is the immigration rate; Mi and Fi are natural and fishing mortality rates of group (i), respectively; ei is emigration rate; and ƒ(Bj,Bi) is a function used to predict consumption rates of predator (j) on prey (i) according to the assumptions of foraging arena theory (Walters and Juanes 1993; Walters and Korman, 1999; Walters and Martell, 2004). A recent multistanza routine (Christensen and Walters, 2004) is used with Ecosim in the Raja Ampat models to impose an equilibrium age structure across age categories for some functional groups (Ainsworth et al., 2007).  BHS EBM PROJECT DATA INTEGRATION We use the Raja Ampat model for the present day as a master version; we adapt it for the sub-area models of Kofiau Island, Southeast Misool Island and the Dampier Strait. In this report, we refer to the presentday Raja Ampat model as representing the year 2005. However most data points in the model originate from the years between 2002 and 2007.  Reef health monitoring Scientific output from the BHS EBM project is used to revise the EwE parameters of Ainsworth et al. (2007). Where possible, reef health monitoring data is used to set the biomass of fish functional groups directly. Recent biomass data is obtained from reef health monitoring studies around the Kofiau and Boo Island groups5 and Misool Island6. Reef health monitoring has been recently completed for Waigeo by field partners in CI (Sorong). Unfortunately, sampling only began in the fall of 2007 and so was unavailable at the time of this work7. The reef health monitoring protocol is available for Kofiau sites in A. Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. E-mail: amuljadi@tnc.org. Unpublished data. 6 M. Syakir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. E-mail: msaykir@tnc.org. Unpublished data. 7 Contact: M. Erdi Lazuardi. CI. Jl Arfak No. 45. Sorong, Papua, Indonesia 98413. E-mail: erdi@conservation.or.id 5  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  9  Pratomo and Setiawan (2006) and Misool sites in Mous and Muljadi (2005). Samoilys (1997) provides additional discussion on the biases and challenges of coral reef stock assessment using transect measurements. Reef health monitoring studies conducted snorkeling and SCUBA transects in monitoring sites selected by a stratified random approach after the methodology of Jolly and Hampton (1990) (see Mous and Muljadi, 2005). Transect sites in Kofiau and Waigeo were selected randomly with replacement from among a population of sites that occur at 3 km intervals along the coast line. One-third of the sites were selected for examination. This is an intensive sampling regime compared to previous TNC efforts in Komodo National Park (Mous and Muljadi, 2005). A similar protocol was designed by TNC staff for Waigeo Island; sampling was conducted by CI staff in this area. Five dive transects were conducted at each site monitored. Herbivorous fish are counted at 4 and 8 m depth. For herbivores > 40 cm tail length (TL), the family is also recorded as surgeonfish (Acanthuridae), rabbitfish (Siganidae) or parrotfish (Scaridae). Piscivorous fish are counted at 12 m depth. The divers searched for 8 piscivore families, but representatives from only 5 were recorded in Kofiau: Carangidae, Serranidae, Lutjanidae, Scobridae and Sphyraenidae. Also recorded was the percent cover for hard coral (live), hard coral (dead), hard coral (bleached), soft coral, macro-algae and ‘other’ substrates. The number of crown of thorn starfish and turtles observed was noted.  Herbivorous fish Abundance counts for herbivorous reef fish are converted to biomass density estimates by calculating the total body weight of observed individuals using length-weight (L/W) relationships and dividing the biomass by the area scanned in the transect. Since herbivorous fish data were recorded at the family level, we use family-specific L/W parameters from Fishbase (FB); they represent the average value of Raja Ampat model species in each herbivorous family. Family growth parameters are in Appendix Table D.4.1. The dives are timed at 4 minutes each. The reef health monitoring protocol in Mous and Muljadi (2005) calls for the diver to swim slowly. We assume that 100 m is covered in one transect, although this distance will vary with current speed8. Also, the snorkeling transects (at 4 m depth, counting small herbivores) will cover more distance on average than the SCUBA transects (>4 m counting large herbivores and piscivores). This may cause us to overestimate the biomass of small herbivores, but we assume the bias is negligible. For herbivorous fish species, divers count the fish occurring to a distance of 5 m on either side of the transect line. Total area scanned is then 1000 m2 per transect. Average individual weights from mean lengths were determined using Equation 2.4.  W = aLb  ( 2.4)  where a and b are species growth parameters found respectively in the ‘a’ and ‘b’ fields of the FishBase (FB) PopGrowth table (selected at the species level), L is total fish length (TL) from sampling in cm and W is body weight in g. Small herbivorous fish were recorded by TNC divers into two size categories, 12.5 cm and 30 cm. The proportion of these individuals was recorded (in percentage) as belonging to the families Scaridae, Acanthidae or Siganidae. These two size categories refer to the median body length9, therefore we assume it is equal to the average body length of fish recorded as required by Equation 2.4. Larger herbivorous fish were recorded individually into one of the following size categories (50, 70, 90 or 120 cm); similarly, we assume that these categories represent average body length. Biomass density at Kofiau and Misool dive sites is calculated as the total observed biomass divided by the area scanned.  Piscivorous fish Biomass calculations for piscivorous fish at Kofiau and Misool Islands are calculated based on the reef health monitoring surveys. Body lengths recorded in transect studies were converted to body weights using the L/W conversion in Eq. 2.4 with species-specific L/W parameters from FB. The L/W coefficient,  Peter Mous. COREMAP II. Jl. Tebet Raya No. 91, Jakarta, Indonesia, personal communication. Mohammad Saykir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413, personal communication. 8 9  10  Chapter 1 Ecosystem Simulation Models of Raja Ampat  a, and the L/W exponent, b, are located respectively in the ‘a’ and ‘b’ fields of the FB PopGrowth table. This information is summarized in Table A.1.1. The biomass density of reef fish species is determined as the total observed biomass divided by the area scanned. As with herbivorous fish, we assume that the length of one transect is 100 m. However, when counting piscivorous fish, the divers were instructed to include all fish in their visible range, not just fish occurring within 5 m of the transect line as was done with herbivorous fish counts (Mous and Muljadi, 2007). This was done because piscivorous fish typically occur in fewer numbers. Therefore, we calculate biomass density for each species based on a transect area that considers the visibility on dives sighting each species. Visibility (V) for dives sighting a given species ranged from 5.3 to 12 m on either side of the transect line in Kofiau, and 1.5 - 20 m in Misool; area scanned is assumed to equal 2V•100 m2. It is determined on a per-dive basis, biomass density is determined for each dive site and species. The biomass density (B) for EwE functional group (j) is calculated as the sum product of the biomass density of reef health monitoring herbivorous or piscivorous fish family (i) and the ratio of the number of species in that family that contribute to the makeup of the EwE group. The total amount of biomass described for reef health monitoring fish families therefore remains the same in the EwE representation (eq. 2.5). The ratio is provided in Table A.1.5.   X ij   B j = ∑  Bi ⋅ i ∑j X ij      (2.5)  Area ratio conversions Biomass calculations for piscivorous fish at Kofiau and Misool Islands are calculated based on the reef health monitoring surveys. Body lengths recorded in transect studies were converted to body weights using the L/W conversion in Eq. 2.4 with species-specific L/W parameters from FB. The L/W coefficient, a, and the L/W exponent, b, are located in respectively in the ‘a’ and ‘b’ fields of the FB PopGrowth table. This information is summarized in Table A.1.1. The biomass density of reef fish species is determined as the total observed biomass divided by the area scanned. As with herbivorous fish, we assume that the length of one transect is 100 m. However, when counting piscivorous fish, the divers were instructed to include all fish in their visible range, not just fish occurring within 5 m of the transect line as was done with herbivorous fish counts (Mous and Muljadi, 2007). This was done because piscivorous fish typically occur in fewer numbers. Therefore, we calculate biomass density for each species based on a transect area that considers the visibility on dives sighting each species. Visibility (V) for dives sighting a given species ranged from 5.3 to 12 m on either side of the transect line in Kofiau, and 1.5 - 20 m in Misool; area scanned is assumed to equal 2V•100 m2. It is determined on a per-dive basis, biomass density is determined for each dive site and species. The biomass density (B) for a given EwE functional group (j) is calculated from the sum of the product of the biomass density of reef health monitoring herbivorous or piscivorous fish family (i) and the ratio of the number of species in that family that contribute to the make up of the EwE group. The total amount of biomass described for reef health monitoring fish families therefore remains the same in the EwE representation (eq. 2.5). The ratio is provided in Table A.1.5.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  11  Reef area ratio Table 2.1 Hard coral coverage reported for Raja Ampat. Area Waigeo Is. Waigeo Is. Waigeo Is. Waigeo Is. Waigeo average Kofiau Is. Kofiau Is. Kofiau average Misool Is. Misool Is. Misool average Avg. Raja Ampat Indonesia  Source McKenna et al. (2002) COREMAP (2001) COREMAP (2005) Donnelly et al. (2003) A. Muljadi (unpublished data) BHS EBM reef health monitoring Donelly et al. (2003) M. Syakir (unpublished data) BHS EBM reef health monitoring Donelly et al. (2003) Donnelly et al. (2003) Spalding et al. (2001)  Average (%) 28.5 45.2 38.9 37.2 37.5  SD 14.8 11.9 32.5 21.6  # sites 44 8 35 25  25.3 30.0 27.7  16.4 22.4  450 35  45.9 30.0 37.9 32.8 1.8  14.4 22.4  53 11  22.9 -  94 -  Biomass for coral groups (azooxanthellate corals, hermatypic scleractinian corals, non-reef building scleractinian corals and soft corals) were assumed to vary between Kofiau, Waigeo and Misool Island study areas in direct proportion to the relative areas covered by hard coral. The area of hard coral coverage is calculated from various sources, including recent BHS EBM reef health monitoring data (Table 2.1). The biomass density of these coral groups is therefore based on the larger Raja Ampat model, and modified for each sub-area by a weighting factor that adjusts for the relative coverage. The coverage of hard coral in Raja Ampat by area (32.8 %) is relatively greater than Kofiau Island (27.7 %) and relatively less than Waigeo (37.5%) and Misool (37.9%) Islands. Biomass density of coral groups is therefore adjusted down for Kofiau (i.e., by a factor of 27.7 / 32.8) and up for Waigeo and Misool. Reef health monitoring data was assembled by Andreas Muljadi (Kofiau Is.) and Mohammad Syakir (SE Misool Is.)10. Reef health monitoring data was collected for Waigeo Is. by M. Erdi Lazuardi11 but was not available at the time of writing of this final report.  Shelf area ratio GIS data summarizes bathymetry as in Fig. A.2.1. The relative area is presented in Table 2.2. Bathymetry was determined using nautical charts held by the Indonesian Navy (TNI AL, 2002) and summarized into GIS format by Mohammad Barmawi12. Table 2.2. Area < 200 m depth. Area Waigeo Kofiau Misool Raja Ampat Indonesia  Shallow area <200 m (%) 38.9 16.6 70.8 58.2 63.4  Deep area < 200 m (%) 61.1 83.4 29.2 41.8 36.6  Source Barmawi, M. (unpublished data) Barmawi, M. (unpublished data) Barmawi, M. (unpublished data) Barmawi, M. (unpublished data) Spalding et al. (2001)  Mangrove area ratio 10 TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. E-mail: amuljadi@tng.org and msyakir@tnc.org. Unpublished data. 11 CI. Jl.Gunung Arfak.45. Sorong, Papua, Indonesia. Email: erdi@conservation.or.id 12 TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia. Unpublished data. Contact: joanne_wilson@tnc.org  12  Chapter 1 Ecosystem Simulation Models of Raja Ampat  GIS data summarizes mangrove coverage as in Fig. A.2.1. The relative area is presented in Table 2.3. The source of the mangrove area data is from LandSat imagry (2000-2002) (NASA Landsat Program, 2006), and it was summarized into GIS format by Mohammad Barmawi. Table 2.3. Area occupied by mangroves. Area Waigeo Kofiau Misool Raja Ampat Indonesia  Mangrove area (km2)  Total area (km2)  46.6 31.5 35.1 455.2 42550.0  6101 2391 4273 45000 2915000  Relative mangrove coverage (%) 0.76 1.32 0.82 1.01 1.46  Source Barmawi, M. (unpublished data) Barmawi, M. (unpublished data) Barmawi, M. (unpublished data) Barmawi, M. (unpublished data) Spalding et al. 2001  Catch and fishing effort parameter revision Catch matrices for the Raja Ampat model, Dampier St., Misool Is. and Kofiau Is. models are provided in Tables D.2.1, D.2.2, D.2.3 and D.2.4, respectively. The catch for Raja Ampat is determined based on governmental statistics assembled in Ainsworth et al. (2007) and includes revised estimates of illegal, unreported and unregulated (IUU) catch for some functional groups made by Varkey et al. (in prep).  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  13  Table 2.4. IUU proxy groups assigned to EwE functional groups EwE group name  Proxy IUU group  Reef assoc. turtles  not changed  Juv. medium reef assoc.  as reef  Green turtles  not changed  Ad. small reef assoc.  as reef  EwE group name  Proxy IUU group  Oceanic turtles  not changed  Juv. small reef assoc.  as reef  Ad. groupers  as reef  Ad. large demersal  not changed  Sub. groupers  as reef  Juv. large demersal  not changed  Juv. groupers  as reef  Ad. small demersal  not changed  Ad. snappers  as reef  Juv. small demersal  not changed  Sub. snappers  as reef  Ad. large planktivore  as half tuna  Juv. snappers  as reef  Juv. large planktivore  as anchovy  Ad. Napoleon wrasse  as reef  Ad. small planktivore  as half anchovy  Sub. Napoleon wrasse  as reef  Juv. small planktivore  as anchovy  Juv. Napoleon wrasse  as reef  Ad. anchovy  as anchovy  Skipjack tuna  as tuna  Juv. anchovy  as anchovy  Other tuna  as tuna  Ad. deepwater fish  not changed  Mackerel  as tuna  Juv. deepwater fish  not changed  Billfish  as tuna  Ad. macro algal browsing  as reef  Ad. coral trout  as reef  Juv. macro algal browsing  as reef  Juv. coral trout  as reef  Ad. eroding grazers  as reef  Ad. large sharks  as shark  Ad. scraping grazers  as reef  Juv. large sharks  as shark  Juv. scraping grazers  as reef  Ad. small sharks  as shark  Detritivore fish  as reef  Juv. small sharks  as shark  Hermatypic corals  not changed  Adult rays  as half of shark  Penaeid shrimps  as invertebrates  Juv. rays  as half of shark  Shrimps and prawns  as invertebrates  Ad. butterflyfish  as reef  Squid  as invertebrates  Juv. butterflyfish  as reef  Octopus  none  Cleaner wrasse  as reef  Sea cucumbers  as sea cucumbers  Ad. large pelagic  as tuna  Lobsters  as lobsters  Juv. large pelagic  as tuna  Large crabs  as invertebrates  Ad. medium pelagic  as tuna  Small crabs  as invertebrates  Juv. medium pelagic  as tuna  Giant triton  as invertebrates  Ad. small pelagic  as anchovy  Herbivorous echinoids  as invertebrates  Juv. small pelagic  as anchovy  Bivalves  as invertebrates  Ad. large reef assoc.  as reef  Sessile filter feeders  as invertebrates  Juv. large reef assoc.  as reef  Epifaunal det. inverts.  as invertebrates  Ad. medium reef assoc.  as reef  Epifaunal carn. inverts  as invertebrates  The IUU estimates themselves are presented in Table D.2.5. The IUU analysis was done for illegal fishing of reef fishes using cyanide and blast fishing; unreported catches were determined for reef fish, anchovy, tuna, shark, lobster and sea cucumber. The percentage level of IUU fishing for reef fish was used as a proxy to approximate the IUU catch for all the reef fish functional groups in the model. Similarly, IUU estimates for tuna, anchovy and shark were used to calculate the IUU in pelagic groups and the estimates for lobster and sea cucumber were used to calculate IUU for the invertebrate groups in the model.  14  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Each functional group in the model was assigned an IUU factor, which represents a certain percentage of reported catch based on the most appropriate proxy IUU group. Table 2.4 shows the IUU factor assigned to each functional group in the EwE model. In addition to incorporating IUU catch, data gathered from three field surveys are used to improve the catch matrix. The three field surveys include an aerial survey of effort conducted in the lifetime of the BHS EBM project by TNC, a resource use survey of Kofiau Is. and a Coastal Rural Appraisal conducted by TNC in 2003. Information from the three surveys is used to refine the distribution of catch between different fishery gear types based on the number of vessels and their sizes.  Aerial survey for fishing effort TNC field teams conducted an aerial survey of fishing effort in Raja Ampat. The survey was conducted in two phases; the first was from January 9 to 13, 2006; the second was from October 18 to 22, 2006. There were 10 flights in each phase to cover all the waters of Raja Ampat. The survey recorded the following point features: vessels (transport, fishing, industrial, tourist, others, unknown), fish cages, fishing shelters, fish platforms, fish aggregating devices (FADs, also known as rumpon), whales, dolphins, manta, dugong, tuna feeding / bait schools. The size of the vessels, the type of engine and the type of activity the vessels were engaged in was also noted. The results from the aerial survey were used as an input in estimating the IUU catches and hence this data contributed to the improvement of the catch matrix in the model. Protocol for the aerial photography survey is provided in Mous (2005); highlights of the aerial survey results are provided in Barmawi (2006a). An online interactive map is available to access the georeferenced aerial photography (www.rajaampat.org); alternatively, a two-DVD set of photographs is available through the TNC Bali office13. Detailed survey results with raw data are provided in Barmawi (2006b) and an additional analysis is forthcoming in the final aerial survey technical report due in 200814. We present here a preliminary analysis of the data in Fig. 2.1 that supports the current catch matrix calculations. Resource use survey The resource use survey for Kofiau Is. in Raja Ampat was conducted by TNC field team for Raja Ampat15. The survey consisted of a mobile monitoring team that traveled by boats to the fishing villages around the Kofiau Is. and also intercepted fishers in the waters around Kofiau. The survey was conducted on 8 days between December 2005 and July 2006. The marine area around Kofiau was divided into 6 sectors totaling an area of 2350 km2 (the average area of each sector: 390 km2). The survey collected information on number and names of vessels observed, the type of activity they were engaged in, the engine types and the gears used when the vessels were found fishing. They also noted the composition and quantity of the fish catch. In addition to monitoring vessels, the survey also monitored fixed gears that included karambas (floating net cages) and temporary huts on water. From the resource use survey it is interesting to see that all of the boats with inboard engines except one are used by Maluku or Sulawesi fishers. All the lobster catch shown in the figure is caught using compressors by fishers from Sulawesi. The fishers from Maluku catch tuna using gillnets while fishers from the 3 villages in Kofiau Is. mostly use troll, longline or bottom gill net. The survey reports a catch of 1477 kg of dry tuna by the Maluku fishers. This is much higher than the tuna caught by the Raja Ampat (RA) fishers. This data point was discarded. Information resulting from the resource use survey is summarized in Fig. 2.2.  Contact: Joanne Wilson, TNC CTC. Jl Pengembak 2, Sanur, Bali, Indonesia joanne_wilson@tnc.org. M. Barmawi. In preparation. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228. Contact: joanne_wilson@tnc.org. 15 Andreas Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Unpublished data. 13  14  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  Type of use  15  Size of vessel  1200 700 600  800  500 400  600  300  400  Type of engine  large decked <50m  small decked <10m  canoe, dinghy  Type of activity 600  600  500  500  400  400  gleaning  unknown  anchoring  0 moving  0  bagans  100  ketinting  100  outboard  200  inboard  200  fishing  300  300  no engine  Number of vessels  small canoe  tourism  passenger  freight  industrial  0  others  0 unknown  100  medium decked <20m  200  200  fishery  Number of vessels  1000  Figure 2.1. Raja Ampat fishing effort from the aerial survey. The figures for the size of vessel, the type of engine and the type of activity includes only the 970 vessels that were found fishing. Source: M. Barmawi (unpublished data); contact: Joanne Wilson, TNC CTC. Jl Pengembak 2, Sanur, Bali, Indonesia joanne_wilson@tnc.org.  Coastal rural appraisal survey (CRA) The coastal rural appraisal survey (CRA) reports about 39% of the catch from Raja Ampat to be reef fish. After the incorporation of the IUU, the catch of reef fish in the model accounts for about 36% of the total catch in the model. This shows that the model catches for reef fish are in agreement with the estimates from local surveys. The model includes more catch for tuna than is reported in the CRA survey, however the CRA publication cautions that the survey did not take into account the tuna catches by the pole and line fishers in Yelu and Misool. Crustacean catches in the model account for about 4% of total catch, whereas the CRA reports that 13% of the total catch consists of lobsters and shrimp. Similarly, the sea cucumber catch in Ainsworth et al. (2007) was much lower than that reported by the CRA.  16  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Type of vessels  40  80  35  70 60  30  50  25  40  20  30  15  20 10  10 5  Type of gears  no engine  slowest outboard engine  Maluku  Deer  Tolobi  Sulawesi  0 Dibalal  big outboard engine  0 inboard engine  Number of fishers  Place of origin  Type of fisheries  70  60  60  40 40  sea turtle  sea cucumber  live fish  lobster  gleaning  fish trap  0  purse seine  10  0  compressor  10  gill net  20  troll / longline  20  tuna etc  30  30  bottom line  Number of vessels  50 50  Fisheries catch 250 200  Catch (kg)  150 100 50  sea turtle  sea cucumber  live fish  lobster  tuna etc  0  Figure 2.2. Resource use survey results. Source: Andreas Muljadi. (TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Unpublished data).  The catch of other invertebrates, such as gastropods, also appears to be underestimated by Ainsworth et al. (2007). The IUU estimates from Varkey et al. (in prep) for invertebrates is reported in Table D.2.5; the revised Raja Ampat model catch is in Table D.2.1. Hook and line gear was not included in the model of Ainsworth et al. (2007) and this catch was aggregated into a more generic gear type called trolling. As it accounts for more than 40% of the catch according to the CRA, we have updated the gear types in the current volume to reflect this. The CRA report states that about 40% of the catch in Kofiau and Misool  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  17  (this study excluded pole and line fishing in Misool) was caught by hook and line fishery. After incorporating the IUU catches the catch matrix had a low proportion of hook and line catch. Hence the catch matrix was further adjusted to increase the component of landings from the hook and line fishery. Comparison of preliminary and revised catch estimates Figure 2.3 compares the revised catch estimate made in this report for Raja Ampat (Table D.2.1), including estimates of IUU catch, with the preliminary catch estimates made for the area (see Table A.3.4 in Ainsworth et al., 2007). The functional groups in the model have been aggregated for this figure. The original precautionary placeholder estimates for illegal and unreported fishing made by Ainsworth et al. (2007) were about 200% of the reported catch for reef fishes and about 400% for anchovy fishery. Fig. 2.3 does not represent this difference; the placeholder IUU values used by Ainsworth et al. (2007) were omitted so the dark grey area in Fig 2.3 shows only the catch determined by those authors from government statistics. Sources of the fishery statistics include the Sorong Regency Fisheries Office, Departemen Kelautan dan Perikanan (DKP); the Raja Ampat Regency Fisheries Office; the Trade and Industry Office (Departemen Perinustrian dan Perdagangan); the Agricultural Quarantine Office, Badan Karantina Pertanian).  Government statistics  100000  IUU estimate (Varkey et al.) Catch (in tonnes)  10000 1000 100  Hermatypic corals  Sessile filter feeders  Ad. coral trout  Detritivore fish  Herbivorous echinoids  Crabs  Ad. Napoleon wrasse  Squid and octopus  Other inverts.  Sea cucumbers  Molluscs  Ad. deepwater fish  Ad. medium pelagic  Ad. small planktivore  Ad. grazers  Ad. butterflyfish  Ad. groupers  Ad. snappers  Ad. small pelagic  Ad. large pelagic  Lobsters  Sharks and rays  Mackerel  Ad.demersal  Shrimps and prawns  Ad. small reef assoc.  Ad. large planktivore  Tunas  Ad. anchovy  Ad. large reef assoc.  1  Ad. medium reef assoc.  10  Functional groups  Figure 2.3. Raja Ampat fisheries catch. The catch shows landings from governmental statistics assembled by Ainsworth et al. (2007) (dark grey area) and the IUU calculated by Varkey et al. (in prep) (light grey). IUU is based on a qualitative methodology, and uses data from the BHS EBM project (aerial surveys, resource use survey and coastal rural appraisal survey). The Y-axis indicates total catch as shown on a log scale.  Table 2.5 shows the IUU incorporated into the revised catch estimates for this contribution, the total catch estimated by (Ainsworth et al., 2007) (including precautionary IUU placeholders) and the final estimate of total catch used in the current Raja Ampat model catch matrix. Note that the IUU estimates for the model functional groups from Varkey et al. (in prep)16 and the total catch in the previous version of the model (Ainsworth et al., 2007) do not add up to the total in the current revised catch matrix (Table D.2.1). This is due to changes made based on the information from the three field surveys described earlier.  16  Varkey, D., Ainsworth, C.A., Pitcher, T.J., Goram, J. (in preparation). Estimating illegal and unreported catches in Raja Ampat Regency, Papua, Indonesia. Contact: d.varkey@fisheries.ubc.ca.  18  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table 2.5. Comparison of preliminary and revised catch estimates for Raja Ampat. Catch is in t•km-2. IUU catch is estimated by Varkey et al. (in prep) based on a subjective methodology. Preliminary catch estimates were made by Ainsworth et al. (2007) and include placeholder estimates of IUU. Column ‘This report’ shows revised catch estimates based on the governmental statistics (assembled by Ainsworth et al., 2007 and including revisions based on BHS EBM field surveys) and estimates of IUU catch. Some catches have gone down after including IUU catches; this is because some precautionary assumptions for unreported catches were included in Ainsworth et al. (2007). The catch estimates with precautionary placeholders are highlighted in grey.  Group Name  IUU total -2  (t•km ) Ad. groupers Sub. groupers Juv. groupers Ad. snappers Sub. snappers Juv. snappers Ad. Napoleon wrasse Sub. Napoleon wrasse Juv. Napoleon wrasse Skipjack tuna Other tuna Mackerel Billfish Ad. coral trout Juv. coral trout Ad. large sharks Juv. large sharks Ad. small sharks Juv. small sharks Ad. rays Juv. rays Ad. butterflyfish Juv. butterflyfish Cleaner wrasse Ad. large pelagic Juv. large pelagic Ad. medium pelagic Juv. medium pelagic Ad. small pelagic Juv. small pelagic Ad. large reef assoc. Juv. large reef assoc. Ad. medium reef assoc. Juv. medium reef assoc. Ad. small reef assoc.  0.069 0.035 0.016 0.084 0.063 0.024 0.023 0.012 0.003 0.260 0.022 0.048 0.037 0.005 0.000 0.019 0.002 0.004 0.000 0.005 0.001 0.043 0.004 0.002 0.023 0.003 0.005 0.002 0.029 0.003 0.266 0.056 0.149 0.027 0.081  Ainsworth This report et al. 2007 total total (t•km-2) (t•km-2) 0.017 0.009 0.002 0.014 0.014 0.003 0.001 0.001 0.000 0.348 0.047 0.064 0.050 0.002 0.000 0.025 0.003 0.006 0.001 0.019 0.002 0.016 0.002 0.001 0.031 0.004 0.007 0.003 0.034 0.004 0.577 0.112 0.350 0.035 0.150  0.094 0.048 0.022 0.114 0.086 0.032 0.031 0.016 0.005 0.608 0.051 0.112 0.084 0.006 0.001 0.045 0.005 0.010 0.001 0.024 0.002 0.059 0.006 0.003 0.054 0.007 0.012 0.005 0.063 0.007 0.362 0.076 0.203 0.037 0.110  Catch increase  Group Name  IUU total -2  (t•km ) 5.5 5.5 11.1 8.3 6.2 10.5 33.0 17.7 26.0 1.7 1.1 1.7 1.7 3.8 3.8 1.8 1.8 1.8 1.8 1.3 1.3 3.8 3.8 3.8 1.7 1.7 1.7 1.7 1.9 1.9 0.6 0.7 0.6 1.1 0.7  Juv. small reef assoc. Ad. large demersal Juv. large demersal Ad. small demersal Juv. small demersal Ad. large planktivore Juv. large planktivore Ad. small planktivore Juv. small planktivore Ad. anchovy Juv. anchovy Ad. deepwater fish Juv. deepwater fish Ad. macro algal browsing Juv. macro algal browsing Ad. eroding grazers Juv. eroding grazers Ad. scraping grazers Juv. scraping grazers Detritivore fish Hermatypic corals Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers Lobsters Large crabs Small crabs Giant triton Herbivorous echinoids Bivalves Sessile filter feeders Epifaunal det. inverts. Epifaunal carn. inverts Total  0.012 0.019 0.005 0.028 0.003 0.005 0.023 0.011 0.017 0.328 0.003 0.008 0.001 0.002 0.000 0.001 0.000 0.062 0.006 0.005 0.001 0.550 0.065 0.024 0.000 0.005 0.132 0.010 0.010 0.002 0.010 0.022 0.004 0.012 0.014 2.860  Ainsworth This report et al. 2007 total total (t•km-2) (t•km-2) 0.015 0.024 0.005 0.028 0.003 0.300 0.030 0.013 0.001 0.509 0.051 0.008 0.001 0.001 0.000 0.000 0.000 0.022 0.002 0.002 0.001 0.145 0.017 0.006 0.000 0.006 0.044 0.003 0.003 0.003 0.003 0.006 0.001 0.003 0.004 3.213  0.016 0.039 0.009 0.057 0.006 0.025 0.025 0.024 0.018 0.356 0.036 0.017 0.002 0.003 0.000 0.001 0.000 0.085 0.008 0.007 0.002 0.695 0.082 0.030 0.000 0.011 0.353 0.013 0.013 0.006 0.013 0.028 0.005 0.015 0.017 4.424  Catch matrices for sub-area models The catch matrices for the three sub-area models were calculated based on three assumptions: • • •  The three areas Kofiau, Misool and Dampier Strait contribute 70% of the catch from Raja Ampat; The catch in each sub-area model is proportional to the biomass density of species groups, the fishers population density and the area of the models; The population density can be used to approximate fishers density.  A value of 70% was assumed based on the fact that the sub area model for Koifiau accounts for all the Kofiau and nearby areas, the model for Misool is located in SE Misool, where almost all the fishery is also concentrated. The catch that is not included is the catch from all parts of Waigeo other than Dampier strait and the catch by fishers from Sorong. The biomass density of the species was calculated based on the results from the reef health monitoring and the area of the habitats available in the sub area models for the different species groups. The population density was used to approximate the fishers density. This population density was obtained  Catch increase 1.1 1.6 1.9 2.0 2.0 0.1 0.8 1.9 12.6 0.7 0.7 2.0 2.0 3.8 3.8 3.8 2.0 3.8 3.8 3.8 2.0 4.8 4.8 4.8 2.0 1.7 8.0 4.8 4.8 1.7 4.8 4.8 4.8 4.8 4.8 1.4  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  19  from Jacinta and Imbir (2007). The population density estimates are as follows: Kofiau 0.9, Dampier St. 1.11 and Misool 1.08 persons•km-2 of model area. The fishers density estimates from the same source were: Kofiau 0.005, Dampier St. 0.22 and Misool 0.08 persons•km-2 of model area. Firman and Azhar (2006) give the following estimates for the three areas respectively: 0.9, 0.88 and 1.88 persons•km-2 and 0.49, 0.46 and 0.98 men•km-2. The statistics bureau (BPS) provides: Kofiau 0.005, Dampier St. 0.22 and Misool 0.08 persons•km-2 of model area. Thus there were several population estimates that we could use, we chose to use the population density from Jacinta and Imbir (2007) as this seemed to be most recent and reasonable. The catch estimates for the Raja Ampat model and the three sub-area models (Dampier Strait, SE Misool and Kofiau Island) are summarized in Figure 2.4.  250  Invertebrates Non-reef Reef fish  3  Catch (t·10 )  200 150 100 50 0 Raja Ampat  Dampier St.  SE Misool  Kofiau  Figure 2.4. Catch for Raja Ampat and sub-area models. Values are estimated in this report based on relative targeted species biomass and human population density.  Cost matrix For estimating the cost for the different fisheries, it was assumed that the fixed cost was 100 dollars per boat. This was approximated from Bailey et al. (2008) who have estimated the cost of boat and net set up to be $156 per year. The costs for the gears were approximated using the fishery costs for different groups obtained from Farid and Anggraeni, 2003. Shore gillnet was assumed to have a similar cost pattern as bagan (lift-net) fishery; diving, cyanide and blast fishing were assumed to have similar cost patterns. The costs were converted to percentage values as is the requirement for Ecopath. The costs matrix for the RA model is shown in Table 2.6.  20  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table 2.6. Cost matrix for the RA model. Costs are estimated as percentage of total revenue for each type of fishery. The last column ‘Cost estimate’ is the estimate on which the cost for each fishery is based.  Type of fishery Spear and harpoon Reef gleaning Shore gillnet Driftnet Permanent trap Portable trap Diving spear Diving live fish Diving cyanide Blast fishing Trolling Purse seine Pole and line Hook and line Lift net Foreign fleet Shrimp trawl  Fixed cost (%) 5.1 0.0 1.3 10.4 0.3 1.7 5.1 5.1 5.1 5.1 11.7 3.1 3.1 5.1 1.3 0.0 0.0  Effort related cost (%) 27.2 45.4 0.0 6.9 37.5 30.1 27.2 27.2 27.2 27.2 20.1 2.9 2.9 27.2 0.0 40.0 40.0  Sailing related cost (%) 9.4 0.0 0.0 16.7 0.0 15.3 9.4 9.4 9.4 9.4 11.4 13.7 13.7 9.4 0.0 40.0 40.0  Profit percent (%) 58.3 54.6 74.8 66.0 62.1 52.9 58.3 58.3 58.3 58.3 56.8 80.3 80.3 58.3 74.8 20.0 20.0  Cost estimate live fish trochus bagan fresh reef fish sea cucumber lobster live fish live fish live fish live fish shark fin mackerel mackerel live fish bagan Ainsworth et al. (2007) Ainsworth et al. (2007)  Price matrix The price matrix was modified using the prices from the valuation report (Dohar and Anggraeni, 2006) which is a summary of the economic valuation of the resources of Raja Ampat. The prices in the previous version of the model were based on the data from the Trade and Industry office in Sorong. The prices from the valuation report (Dohar and Anggraeni, 2006) were used because they were better estimates of local price and were more recent than the data from the Trade and Industry office. The price matrix for the RA model is shown in Table 2.7.  Fisher interviews Between the months of September and December in 2006 fisher interviews were conducted in Raja Ampat (SE Misool Is. and Kofiau Is.) by field staff from Conservation International (CI) and the State University of Papua (Universitas Negeri Papua, UNIPA) with the aim of gathering local ecological knowledge (LEK) regarding the exploitation and population status of fish, invertebrates, reptiles and mammals. This LEK information has been used in the current modeling study to establish the likely abundance trend for functional groups and the unexploited biomass for some. Two hundred and nine fisher interviews were conducted in 13 villages (Table 2.8) using a convenience sampling approach. Fishers were interviewed opportunistically at workshops and other functions under the TNC Coastal Rural Appraisal survey17. A list of villages sampled in Misool and Kofiau Islands is available in Muljadi (2004).  17  J. Wilson. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228. Unpublished data.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  21  2.89  Shrimp trawl  2.89  Foreign fleet  2.89  Lift net  7.71  2.89  Hook and line  7.71  Pole and line  Blast fishing 18.57  68.40  Purse seine  Diving cyanide 68.40  68.40  Trolling  Diving live fish 68.40  7.71  Portable trap  Diving spear  Permanent trap  50.52  7.71  Driftnet  50.52  7.71  Shore gillnet  7.71  Reef gleaning  Group names Ad. groupers Sub. groupers Juv. groupers Ad. snappers Sub. snappers Juv. snappers Ad. Napoleon wrasse Sub. Napoleon wrasse Juv. Napoleon wrasse Skipjack tuna Other tuna Mackerel Billfish Ad. coral trout Juv. coral trout Ad. large sharks Juv. large sharks Ad. small sharks Juv. small sharks Adult rays Juv. rays Ad. butterflyfish Juv. butterflyfish Cleaner wrasse Ad. large pelagic Juv. large pelagic Ad. medium pelagic Juv. medium pelagic Ad. small pelagic Juv. small pelagic Ad. large reef assoc. Juv. large reef assoc. Ad. medium reef assoc. Juv. medium reef assoc. Ad. small reef assoc. Juv. small reef assoc. Ad. large demersal Juv. large demersal Ad. small demersal Juv. small demersal Ad. large planktivore Juv. large planktivore Ad. small planktivore Juv. small planktivore Ad. anchovy Juv. anchovy Ad. deepwater fish Juv. deepwater fish Ad. macro algal browsing Juv. macro algal browsing Ad. eroding grazers Juv. eroding grazers Ad. scraping grazers Juv. scraping grazers Detritivore fish Hermatypic corals Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers Lobsters Large crabs Small crabs Giant triton Herbivorous echinoids Bivalves Sessile filter feeders Epifaunal det. inverts. Epifaunal carn. inverts  Spear and harpoon  Table 2.7. Raja Ampat price matrix. Price values are in 103 Rp•kg-1. The grey cells are the prices based on (Dohar and Anggraeni, 2006); unshaded prices are from Ainsworth et al. (2007).  50.52  7.71  7.71  31.13  31.13  31.13  15.57  31.13  7.71  7.71  31.13  31.13  31.13  7.71  31.13  2.25  2.25  2.25  2.25  2.25  2.25  120.00  120.00  60.00  120.00  120.00  120.00  60.00  120.00  21.46  21.46  2.89  21.46 9.44  9.44  9.44  9.44  3.16  3.16  3.16  3.16  5.17  5.17  5.17  5.17  10.17 7.71  7.71  7.71  7.71  7.71  7.71  2.90  2.90  2.90  2.90  2.90  2.90 5.84 3.30 4.32 4.32  3.28  3.28  4.93  4.93  1.00  1.00  1.00  1.00  7.71  7.71  7.71  12.50  12.50  7.71  7.71  7.71  7.71  7.71  7.71  0.90  0.90  0.90  0.90  0.90  2.90  2.90  3.03  3.03  3.03  2.90  2.90  2.90  2.90  2.90  2.90  2.90  3.04  3.04  3.04  2.90  2.90  2.90  2.90  1.34  1.34  1.34  1.34  1.34  1.34  1.34  1.34  7.71  7.71  7.71  13.44  2.90  2.90  2.90  2.90  2.90  7.71  7.71  7.71  13.33  13.33  2.90  2.90  2.90  2.90  2.90  2.90  2.90  3.22  2.90  2.90  2.90  12.50  7.71  2.90  13.44  13.44  7.71  1.34 1.34 13.44  2.90  2.90  7.71  13.33  2.90  2.90  2.90  3.22  3.22  3.22  2.90  2.90  2.90  2.90  2.90  3.04  3.04  3.04  3.04  2.90  2.90  2.90  2.90  2.90  2.90  3.11  3.11  3.11  2.90  13.33  2.90  2.90  2.90  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  8.75  5.19  5.19  5.19  5.19  5.19  5.19  5.19  5.19  5.19  5.19  2.90  2.90  3.47  3.47  2.90  2.90  2.90  2.90  7.71  7.71  8.69  8.69  2.90  2.90  2.90  2.90  2.90  2.90  3.47  3.47  2.90  2.90  2.90  2.90  2.90  2.90  3.47  3.47  2.90  2.90  2.90  2.90  7.71  7.71  8.69  8.69 0.00 50.00 7.16 25.72  6.50  6.50  67.99  67.99  65.85  122.50  122.50  122.50  32.50  32.50  32.50  4.05  4.05  4.05  4.05  4.05  4.05  30.000  30.000  30.000  6.08  6.08  6.08  1.15  1.15  1.15  1.15  1.15  1.15  15.00 1.15  22  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table 2.8. Fisher interviews conducted in Raja Ampat for abundance trend study.  The questionnaire form used by CI/UNIPA has been translated into English and presented in Ainsworth et al. (2007) (Appendix C.1). Data Village District # interviews fields include a qualitative ranking of abundance Yelu SE Misool 9 for commercial and artisanal fish and Gamta SE Misool 12 invertebrate families, and charismatic animals Fafanlap SE Misool 3 including reptiles (turtles and crocodiles), birds Fishouys SE Misool 1 and mammals (Mysticetae, Odontocetae and Harapan jaya SE Misool 6 dugong, Dugong dugon). Fishers characterized Lilinta SE Misool 20 Usaha jaya SE Misool 20 the abundance of each family or species group Kapacol SE Misool 18 into one of three categories (high, medium or Dibalal Kofiau Is. 30 low) for each of the time periods 1970, 1980, 1990 Tomolol SE Misool 20 and 2000. We also asked them to score three Biga SE Misool 18 yes/no depletion indicators referring to whether Tolobi Kofiau Is. 26 the interviewees had noticed a reduction in the Deer Kofiau Is. 24 abundance of each family or species group, blank 2 whether they have noticed a size reduction, and Total 209 whether there had been a price increase. For the price increase indicator, an approximate year was also recorded representing when the price increase took effect. Fuzzy expert system for LEK abundance quantification A new fuzzy logic expert system is developed by Ainsworth et al. (2008) to convert the qualitative interview abundance information concerning family and species groups to quantitative scores of relative abundance. A fuzzy logic method was chosen in order to systematically address the potential bias of between-fisher interpretations of abundance categories. That is, fishers may hold different perceptions regarding what constitutes ‘high’, ‘medium’ or ‘low’ abundance. The interpretation may vary with fisher experience, gear type or fishing sector specialization, or some other demographic descriptor. The interpretation may also vary with the species group under review. For example, the abundance change in targeted species to which fishers owe their earnings or family’s sustenance, may be perceived differently than in untargeted species that hold no commercial or nutritional value. Having generated a time series of perceived relative abundance change from 1970 to present using the fuzzy logic algorithm, the output results, which are categorized by taxonomic family or species group, are converted into relative abundance trends for EwE functional groups. For each period (1970, 1980, 1990 and 2000), the relative abundance of a EwE functional group is assumed to be represented by a weighted average of the abundance scores for relevant families or species groups. The weighting factor applied to each family is proportional to the number of species in that family contributing to the EwE functional group, as a fraction of the total number of contributing species that are described by the LEK data. The abundance score for a EwE functional group (Aj) is therefore calculated as the sum product of the family abundance score (Ai) and the ratio of the number of species (X) in family (i) belonging to the EwE functional group (j) (Eq. 2.6). The ratios are provided in Table A.1.4.   X ij   A j = ∑ Ai ⋅  i ∑i X ij    (2.6)  Ainsworth et al. (2008) used the LEK data trends to back-calculate the relative biomass of functional groups in 1970. Their methodology used CPUE data trends (from 1990-2005) to scale the output from the fuzzy logic algorithm and establish an absolute range of biomass change between 1970 and 2000. These authors assumed that the decline in CPUE between 1990 and 2000, which was quantified by Ainsworth et al. (2007), is representative of the proportional decline in biomass between those periods. They scaled the LEK trend so that 1990 and 2000 values agree with the CPUE values for those periods, and maintaining the ratio between all time periods derived from the fuzzy logic algorithm, this provided an estimate of biomass for 1970 and 1980. For many functional groups, the 1970 biomass was assumed to be similar to the unexploited biomass (B0), and this allowed Ainsworth et al. (2008) to reconstruct the unfished ecosystem for Raja Ampat. They established a potential range for the unexploited biomass by combining  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  23  this LEK-based estimate with predictions of B0 derived from the present-day Raja Ampat model described in this report.  Gut content analysis In November and December 2006 an analysis of fish gut contents was conducted in Raja Ampat by CI staff and two students from UNIPA18. The protocol for obtaining samples, dissecting stomachs and analyzing the results is presented in Appendix C.2 of Ainsworth et al. (2007). Briefly, fish were purchased at markets and the stomachs removed, or else fishers were paid a fee in order to extract the stomachs. Stomachs were preserved in formalin and later dissected in the lab. Prey items were weighed and identified to the species or family level. The protocol was devised especially to support the current EwE models, so it was not important to identify prey species beyond the functional group level. Nevertheless, taxonomies were identified to a more precise level in order to make the data more valuable to future scientific studies. The diets of predator fish families are converted to percent composition values and scaled to total 100%. The following assumptions were made in order to apply the stomach content data to the EwE models: Fish were included as part of the diet of several species of small coral fish. We therefore split the fish component into the following groups: large reef associated (20%), medium reef associated (20%), small reef associated (30%), macro-algal browsers (10%), eroding grazers (10%) and scraping grazers (10%). Entries for shrimp were divided equally between the two shrimp groups, ‘penaeid shrimps’ and ‘shrimps and prawns’. There were sand and coral fragments in the diet of several families of fish sampled. Half of this amount was assumed to be biogenic, originating from the hard coral functional group ‘Hermatypic scleractinian corals’; the other half was assumed to be sand and was omitted from the diet matrix. We assumed those species that ate hard coral would also eat soft coral and non-reef building scleractinian corals. We assumed that those species would eat about half as much soft coral and non-reef building coral. There were several entries for unidentifiable brown liquid. These were omitted from the diet composition. Diet information for families is distributed into 22 functional groups using conversion ratios in Table C.1.1. The conversion is based on the relative number of species from each family contributing to the composition of the aggregated EwE functional groups. The diet information collected from stomach samples is compared with the results of the diet allocation algorithm19 developed for Raja Ampat by Ainsworth et al. (2007), and with the final Raja Ampat model presented in Ainsworth et al. (2007) after balancing and tuning to time series data. Numerous ad hoc changes that were made to the Raja Ampat diet matrix during balancing and tuning would hopefully have maneuvered the model closer to a state representing reality. The rank order of diet items is compared in order to study the difference between the prediction of the diet algorithm and the stomach data collected. There are a total of 156 interactions common to both the data sets. These interactions were scored for each data set as a rank (out of 156) where low ranks indicate a major diet component. The squared difference in ranks between the data sets was used as an indicator of agreement so that a low sum of squares (i.e., for all prey items combined) indicated high agreement between the two diet composition sources. The upper quartile of squared rank differences is assumed to represent critical disagreement in interactions between the two data sets (Fig. 2.5). Below this level, the two data sets are assumed to be in agreement.  Contact: Christovel Rotinsulu. CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia. E-mail: chris@conservation.or.id The diet algorithm processes FishBase diet information to allocate generic or imprecise prey categories into EwE functional groups using habitat information, predator gape size and prey body size (Ainsworth et al., 2007). 18 19  24  Chapter 1 Ecosystem Simulation Models of Raja Ampat  30  Frequency  25 20 15  Instances where stomach sampling contradicts RA model  10 5 0 0  1  4  9  16  25  36  49  64  81  100 121  144 169  196  225  Square of difference in ranks Figure 2.5. Agreement between Raja Ampat model and stomach sampling diet composition data. Raja Ampat model diet parameters are based on an allocation algorithm and modified by balancing and tuning (Ainsworth et al. 2007). The upper quartile of interactions (dark grey bars) represents instances where stomach sampling strongly contradicts Raja Ampat model. 37 Raja Ampat model interactions are contradicted by stomach data.  Table 2.9 indicates which interactions in the Raja Ampat model and in the original diet algorithm results are contradicted by the stomach sampling information. Where stomach data contradicts the diet algorithm interactions are marked with an ‘A’; these may represent where the process of balancing and tuning the models corrected diet errors in the algorithm. There are 48 diet algorithm interactions contradicted by the stomach data. Where stomach data contradicts the final Raja Ampat model interactions are marked with an ‘M’; these represent a necessary increase in residuals versus the ground-truthing stomach sample data in order to balance the model. There are 37 Raja Ampat model interactions contradicted by the stomach data. Only interactions for which the stomach data contradicts both the Raja Ampat model and the original diet allocation algorithm were revised here. These are marked by ‘Both’ in grey cells; there are 29 interactions. Table 2.10 demonstrates the direction of disagreement between stomach sample data and the final Raja Ampat model diet matrix used by Ainsworth et al. (2007). The direction of disagreement determines what change must be made to the Raja Ampat diet matrix for the current revision. Among the 56 interactions marked in Table 2.10, the stomach sampling data contradicts only 3 interactions in different directions with respect to the Raja Ampat model and the original diet algorithm. Adjustments made during the process of balancing and tuning the model therefore had a minimal impact on the accuracy of the diet matrix, as revealed by the stomach sampling data.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  25  Table 2.9 Raja Ampat model and diet algorithm disagreement versus stomach sampling data. M: stomach data contradicts model; A: stomach data contradicts algorithm; BOTH: stomach data contradicts model and algorithm. Grey cells indicate interactions modified by current diet matrix revision. Prey / Predator Large reef assoc. Medium reef assoc. Small reef assoc. Macro algal browsing Eroding grazers Scraping grazers Hermatypic corals Soft corals Shrimps and prawns Squid Octopus Large crabs Small crabs Bivalves Epifaunal det. inverts. Epifaunal carn. inverts Infaunal inverts. Large herb. Macro algae Sea grass  Snap’rs  Skipj’k tuna  Other tuna  Large sharks  B’fly fish  Large pelagic  M BOTH BOTH  Large reef assoc.  Med. reef assoc.  A  BOTH A A BOTH BOTH A BOTH  A BOTH A  BOTH BOTH  BOTH M  Large plank.  Deep. fish  M  A BOTH BOTH  A A BOTH  M M  BOTH BOTH A  A  A M A A BOTH BOTH M  M A A  BOTH BOTH  BOTH  A A A BOTH BOTH  BOTH  BOTH BOTH  BOTH  BOTH BOTH BOTH  Table 2.10. Direction of disagreement between model and stomach sampling data. “+”: stomach samples indicate a greater proportion of this prey than was predicted by the Raja Ampat model; “-”: samples indicate less prey. Grey cells indicate interactions modified by current diet matrix revision.  Prey / Predator Large reef assoc. Medium reef assoc. Small reef assoc. Macro algal browsing Eroding grazers Scraping grazers Hermatypic corals Soft corals Shrimps and prawns Squid Octopus Large crabs Small crabs Bivalves Epifaunal det. inverts. Epifaunal carn. inverts Infaunal inverts. Large herb. Macro algae Sea grass  Snap’rs  Skipj’k tuna  Other tuna  Large sharks  B’fly fish  Large pelagic  -  Large reef assoc.  Med. reef assoc.  +  -  -  -  + +  Large plank.  Deep. fish  -  -  + + -  -  -  + -  -  + + + +  + + + +  +  + -  + +  +  + + +  26  Chapter 1 Ecosystem Simulation Models of Raja Ampat  ECOPATH BALANCING Present-day Raja Ampat model Revisions made to the 2005 Raja Ampat catch matrix to include better estimates of illegal and unreported catch forced the model out of balance for the following commercial groups: groupers, snappers, Napoleon wrasse, large and medium pelagics and lobsters. Although Ainsworth et al. (2007) had included conservative placeholder estimates of unreported catch for the three reef fish groups (amounting to 60%, 50%, 100% of reported catch for groupers, snappers and Napoleon wrasse, respectively), the addition of more qualified IUU estimates by Varkey et al. (in prep.) increased fishing mortality (F) by an average of almost 15 times for these functional groups across age stanzas. To maintain the improved catch estimates we could either permit an instantaneous biomass decline, which we did do to some degree for all highly commercial groups, or increase the productivity of these groups by altering the production rate (P/B), biomass, or both. The production rate for these long-lived species groups should remain low however, and the P/B values from Ainsworth et al. (2007) (adult P/B = 0.225, 0.4 and 0.5 yr-1, respectively) cannot be increased enough to reasonably provide the additional production required by fisheries. Fortunately, better biomass estimates have since become available from the BHS EBM reef health monitoring project for reef fish species20. The data tend to indicate higher biomass densities than were estimated in Ainsworth et al. (2007) and, on the whole, the revised biomass estimates satisfy the production demands from IUU fisheries. Ecotrophic efficiencies were set to 95% for these reef fish groups, the revised biomass data were entered, and the resulting biomass accumulation rates were deemed to be acceptable if they satisfied the constraint that fisheries could remove no more than 60% of a group’s total annual biological production. A similar criterion was used for large and medium pelagics and lobsters. Groupers The grouper functional groups experienced at least a 5.4 times increase in fishing mortality as a result of adding the IUU catch compared to the model of Ainsworth et al. (2007). To allow for the additional biological production required in the adult group, we held the P/B rate as previously estimated (0.225 yr1). This is a low production rate compared with some published grouper statistics (e.g., 0.37 yr-1, Caribbean coral reef, Opitz 1993; 0.37 yr-1, Great Barrier Reef, Gribble, 2001; 0.45 yr-1, Gulf of Mexico, ArreguínSánchez et al., 1993a), however it may be appropriate for Raja Ampat if the average body size remains large after historically light exploitation. Instead, we increased biomass from 0.184 t•km-2 to 0.5 t•km-2. The old value was based on transect fish counts in S. Waigeo island (COREMAP 2005). The new value is also based on this data, but in addition incorporates reef health monitoring survey data from Kofiau and SE Misool (Tables A.1.1 and A.1.2). Biomass densities for these three areas are scaled proportionately to account for their relative reef coverage, and then the value is averaged to represent Raja Ampat. The resulting biomass density for adult groupers in Raja Ampat, 0.518 t•km-2, is reduced to 0.5 t•km-2 in order to obtain a similar instantaneous rate of biomass decline as calibrated in the previous technical report. Biomass for subadult and juvenile stanzas is calculated using the existing EwE age structure model for a combined grouper biomass density of 0.699 t•km-2 (Table D.1.1). Fisheries remove about 6% of the available annual production, resulting in a ~2% loss of stock size per year in the initial simulation years. Snappers The snapper functional groups experienced a 6-10 times increase in fishing mortality as a result of adding IUU catch. To allow for the additional biological production required in the adult group we held the P/B rate as previously estimated (0.4 yr-1), although, according to previous tropical EwE studies, the value for lutjanids could potentially be higher (e.g., 0.7 yr-1, Pauly and Christensen, 1993; 0.44 yr-1 ArreguínSánchez et al., 1993b) or lower (0.3 yr-1, De La Crus-Agüero, 1993; 0.36 yr-1, Arreguín-Sánchez et al., 1993a). Instead, we increased biomass from 0.081 t•km-2 to 0.345 t•km-2. The old value was based on transect fish counts in S. Waigeo island (COREMAP 2005). The new value incorporates reef health monitoring data from SE Misool (Table A.1.2). The Kofiau data exhibited high densities for lutjanids, 2.53 t•km-2 due mainly to Lutjanus rivulatus. We assume that this is not representative of Raja Ampat. In fact, the model could not easily be made to accommodate such a high biomass. An average biomass density was therefore calculated without the Kofiau data point, using SE Misool reef health monitoring and COREMAP Kofiau: A. Muljadi; SE Misool: M. Syakir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Email: msyakir@tnc.org. Unpublished data.  20  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  27  (2005) estimates. The value was standardized to reflect the relative reef coverage in SE Misool versus Raja Ampat. Biomass density for subadult and juvenile groups is determined with the existing age structure parameters, and an overall biomass for snappers is estimated to be 0.651 t•km-2 (Table D.1.1 Biomass). Fisheries remove about 12% of the available annual production, resulting in a ~1% loss of stock size per year in the initial simulation years. Napoleon wrasse The Napoleon wrasse (Cheilinus undulatus) functional groups were affected most by the addition of the unreported catch. Despite the placeholder estimate for IUU used by Ainsworth et al. (2007), which was 100% of reported catch, the fishing mortality increased on this group by 33 times with the addition of IUU catch. We opted to keep the revised estimates of catch, reducing predation mortality and increasing biomass. Predation mortality on adult Napoleon wrasse was reduced from 0.4 to 0.2 yr-1. This helped offset the impact of additional fishing mortality and allowed the biomass accumulation rate to stay close to the previously calibrated level. The reduced value for predation mortality now lies closer to the values used for grouper, snapper and large reef associated adult fish (F = 0.038, 0.156 and 0.225 yr-1, respectively). Predation mortality should be highest in the large reef associated fish group because it contains smaller species on average than the more selective groups: groupers, snappers and Napoleon wrasse. We also increased the biomass estimate of Napoleon wrasse in the Raja Ampat model from 0.034 t•km-2 to 0.152 t•km-2. The previous estimate simply assumed 10 fish per hectare in reef environments (Russel, 2004); the new estimate is based on species-level identification of Cheilinus undulatus in reef health monitoring transects in SE Misool. The calculated value of 0.166 t•km-2 (Tables A.1.1 and A.1.2) was assumed to include adult and sub-adult stanzas. The figure was then scaled to reflect the relative shelf area (<200 m depth) in SE Misool and Raja Ampat; biomass density was reduced slightly to represent the comparatively deep area of Raja Ampat. The new biomass estimate for Napoleon wrasse is divided into three age stanzas according to the existing multi-stanza model (Table D.1.1). Fisheries remove 47% of the available annual production, resulting in an initial 8% annual biomass decline in forward simulations of the adult group. The decline stabilizes in 5-10 years. Pelagic fish Once we incorporated IUU catch, the fishing mortality on large and medium pelagics increased by 1.75 times; the discrepancy is not severe as in reef species. To permit the higher rates of capture in the large pelagic group we increased the biomass pool from 0.054 t•km-2 (adult stanza) to 0.074 t•km-2. The previous estimate was determined using an approximate method where abundance of large pelagic species was inferred using qualitative rankings of abundance found in McKenna et al. (2002), and then absolute biomass was estimated using species-level anchor points from transect counts (COREMAP, 2005). The revised biomass estimate includes this value, but it is now averaged along with values from Kofiau and SE Misool Islands. Values from Kofiau and SE Misool Islands were determined by scaling the Raja Ampat biomass value in direct proportion to the relative amount of sea area in each local area model, so that models containing relatively less sea area have lower abundances of pelagic fish overall. Biomass in the juvenile group is determined based on the adult biomass using previous multistanza parameters. Total large pelagic biomass is 0.122 t•km-2 (Table D.1.1). Fisheries remove 14% of the available annual production, which causes an initial biomass decline for the adult group of about 5% per year in the first few simulation years. Similarly, the biomass of medium pelagics was increased from 0.011 t•km-2 (adult stanza) to 0.030 t•km-2 to make more production available to fisheries. The new biomass term includes information from Kofiau and SE Misool reef health monitoring studies. It now represents an average of transect biomass densities for these areas, and the biomass level previously estimated for Raja Ampat from qualitative sources (as large pelagics; McKenna et al. 2002). Biomass in the juvenile group is determined based on the adult biomass using previous multistanza parameters. Total medium pelagic biomass is 0.122 t•km-2 (Table D.1.1). The biomass accumulation rate is slightly positive under baseline levels of fishing effort, but biomass quickly assumes an equilibrium position under baseline dynamic simulations that is close to the baseline level. The skipjack catch calculated by a CI valuation report (Dohar and Anggraeni, 2007) looks at catches from only 2 tuna companies out of 150 operating in the area, so the catch estimates may be unrepresentative of  28  Chapter 1 Ecosystem Simulation Models of Raja Ampat  the total amount from Raja Ampat. We have therefore elected to use DKP and Trade and Industry Office statistics collected in Indonesia (Ainsworth et al., 2007). These government sources are likely to include skipjack catch from regions outside of Raja Ampat such as the Halmahera Sea, Seram Sea, Maluku, Cendrawasih Bay, Fak-fak, Kaimana and elsewhere in the Pacific Ocean21. However, we assume that records kept in Sorong will be more representative of Raja Ampat. The gross quantity of catch was typically adjusted upwards to account for IUU (Varkey et al., in prep.). Lobsters The only invertebrate group heavily affected by the addition of IUU catch is lobsters. Considering the IUU estimates, a total of 0.262 t•km-2 of lobsters is captured in reef gleening operations, and 0.354 t•km-2 is captured in Raja Ampat over all gear types (Table D.2.1). Although important in commercial and artisanal fisheries, this high level of catch is at least an order of magnitude more than the original lobster groups that the model of Ainsworth et al. (2007) could accommodate. In order to resolve the discrepancy, we opted to increase both the biomass and production rate of the adult functional group. Biomass and production rate were both highly uncertain data points, and their values may have been improved by the additional constraint of unreported fisheries catch. The previous biomass estimate, 0.219 t•km-2, was calculated by Ainsworth et al. (2007) from reef top transects along the South coast of Waigeo Is. (COREMAP, 2005). Although it is based on sampling, an approximate scaling factor was used by Ainsworth et al. (2007) to convert the Waigeo abundance into Raja Ampat biomass density; the scaling factor depends on the assumption that lobsters occupy mainly reef areas. This is a potential source of error. We increased biomass to 0.5 t•km-2. Put in context, this value represents about 87% of crab biomass in the model, and about 51% of sea cucumber biomass. The previous P/B for lobsters, 0.446 yr-1, is based on an empirical formula (Brey, 1995) calculated using life history parameters from four Australian genera (BRS, 1999). We increased the P/B substantially to 0.8 yr-1 in order to agree with the large biological production rate predicted by our revised fishery estimates. This estimate is not unreasonably high compared with values used by other authors in tropical systems (e.g., Mexico: 0.9 yr-1, Arreguín-Sánchez, 1993b; 0.62 yr-1, Vidal and Basurto, 2003). Still higher values (~3 yr-1) are typically used for aggregated groups of heterotrophic benthos (e.g., Sivestre et al., 1993).  Raja Ampat model for 1990 Reef fish The addition of revised IUU estimates from Varkey et al. (in prep) improved the catch values for reef fish used by Ainsworth et al. (2007) for all Ecopath models including the 1990 Raja Ampat model. The revised catch estimates for 1990 include year-specific IUU data, as estimated based on the historical trend of misreporting. With few exceptions, adding the revised catch estimates did not greatly disturb commercial reef fish groups. That is, the rate of production in the preliminary 1990 model was generally sufficient for target functional groups to supply the revised fishery catches, unlike with the 2005 model. However, 1990 biomasses for Raja Ampat were also revised and entered into the model. The 1990 biomasses for reef fish were estimated using the same methodology as Ainsworth et al., 2007, in which past biomass is determined relative to the 2005 level on the basis of CPUE data from government fishery statistics. With the incorporation of these new predator biomass values, consistently revised upwards from the preliminary estimates based on results of the reef health monitoring study in Kofiau and SE Misool, many invertebrate groups seemed over-predated. To balance the 1990 model, we increased the biomass of infaunal invertebrates from 27.4 t•km-2 to 35 t•km-2. We also broadened the diet of some predators to provide additional resources to them. It was necessary to reduce the biomass of snappers in 1990 below the amount estimated using the CPUE conversion; also for medium reef associated fish. Revised biomass estimates for these groups are provided in Table D.1.1. Sharks Addition of the IUU catch increased fishing mortality on adult large sharks by 20% over the level estimated by Ainsworth et al. (2007); juvenile fishing mortality was increased by more than 3 times. Combined with reduced prey availability following the catch and biomass revisions, these changes in the  21  Anita Gracia. CI. Jl.Gunung Arfak.45.Sorong, Papua, Indonesia, personal communication.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  29  influential group large sharks introduced system-wide instability to the 1990 model. It was resolved by altering the age-class mortality parameters (Z) (adults: 1.1 to 0.7 yr-1; juveniles: 1.3 to 0.9 yr-1) so that juveniles compose a greater fraction of the total population biomass. This reduced fishing mortality on them. This change was also incorporated in the 2005 Raja Ampat model since it is likely that the population of large sharks is now similarly skewed towards juvenile age classes as a result of heavy exploitation. Finally, it was necessary to significantly reduce predation mortality through the diet matrix on juvenile large sharks, juvenile small demersals and juvenile coral trout as a consequence of increased predator biomasses recorded in reef health monitoring data. Turtles Several changes were required to recreate the observed decline in turtle populations for all turtle groups (reef associated, green turtles and oceanic turtles) which, in initial tests of the revised 1990 model, failed to deplete as is thought to have happened in Raja Ampat since 199022. The production rates (P/B) for these groups were reduced relative to the 2005 model to reflect the prevalence of larger individuals in 1990. The P/B values, 0.143 yr-1 for reef associated, 0.053 yr-1 for green turtles and 0.05 yr-1 for oceanic turtles, used by Ainsworth et al. (2007) for both 1990 and 2005 models, have been reduced to 0.09, 0.03 and 0.03 yr-1, respectively in the 1990 model. Although we have no reliable biomass time series for any turtle group, the dynamics now fall close to the estimated 1990 and 2005 start/end points. Following the same logic, the consumption rates (Q/B) have been reduced from the preliminary estimates of 3.5 yr-1 to 3.0 yr-1 for all turtle groups. Catch rate was increased from the preliminary estimate of Ainsworth et al. (2007) from 2, 1.1 and 1.1 kg•km-2 to 8, 6 and 6 kg•km-2, respectively. Finally, ecotrophic efficiency was lowered from 0.95 in the preliminary model for all groups, to 0.4 (reef associated and green turtles) and 0.1 (oceanic turtles). These lower values are more appropriate for wide ranging species; lower values reflect a substantial proportion of mortality that occurs outside of the modelled system. For a discussion on the challenges of modelling migratory behaviour in Ecosim see Martell (2004). The oceanic turtles, including the wide-ranging species leatherback turtle (Dermochelys coriacea) represent an extreme example of migratory species. A recent study in Raja Ampat confirms anecdotal reports with regards to the importance of the area for leatherbacks, and especially North Papua as a nesting area, a migration corridor, and perhaps also a breeding and foraging area (Hitipeuw et al., 2007). Although too few individuals have been tagged to draw conclusions regarding the population status, the wide-ranging nature of these animals and their use of Raja Ampat habitat for a variety of purposes are confirmed in this study. The BHS EBM turtle tracking and monitoring project indicates that green turtles too (in the EwE reef-associated functional group), are known to range in and out of Raja Ampat. One animal (named Mona by WWF staff) was tracked as far away as Borneo in a time span of only 60 days23. The implicit assumption for these and other wide-ranging species is that the amount of fishing and predation mortality in the modelled system is similar to the mortality sources outside of the system. The trophic impact of turtles should be adequately represented in the models; however, estimates regarding the population resiliency, especially with regards to fishing activity, are not easily represented except by applying the assumption that the level of fishing activity in the modelled area is representative over their entire range.  Sub-area models Ecosim models were prepared as the basis for four Ecospace models presented here: Raja Ampat, Dampier Strait, SE Misool Island and Kofiau Island. Catch for the sub-areas was apportioned according to the methodology reported above; a scaling factor was used to adapt the Raja Ampat catch matrix to the subareas, which is based on the relative biomass of targeted species and the human population density. However, some manual adjustments were required for the sub-area models to correct substantial imbalance. Most of the difficulties in balancing the sub-area models were related to excessive predation mortality caused by the input of reef health monitoring transect biomass data (the biomass estimate for many functional groups was revised upwards based on reef health monitoring data). We considered reef health monitoring data to be high quality, and so to achieve balance we adjusted the diet matrix to relieve the excessive predation. In only a few cases it was necessary to reduce the amount of catch estimated for the sub-area models.  22 23  Andreas Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413, personal communication. Geoffrey Gearheart; WWF Pejaten, Tabanan, Bali, personal communication; satellite tracking data available at SeaTurtle.org.  30  Chapter 1 Ecosystem Simulation Models of Raja Ampat  In order to balance the Kofiau model we reduced the catch of Napoleon wrasse to 20% of the level estimated above using target species biomass and human population density. We also increased biomass density, which was modified from the Raja Ampat value using relative reef area ratio (Table 2.1), by 67% to 0.015 t•km-2. This further reduced fishing mortality on Napoleon wrasse so that fisheries consumed half of the available surplus production. This amount is representative of a fully exploited species. Similarly, we reduced the catch of adult large demersals, adult small demersals and skipjack tuna to 50% of the values estimated using ratios above. We reduced mackerel catch to 40% of its preliminary value. Except for these adjustments which required us to reconsider catch, all other adjustments to the Kofiau model for the purposes of balancing were made using the diet matrix.  ECOSIM TUNING Vulnerability parameterization Vulnerabilities for the 1990 model were parameterized initially using an automated search algorithm (Christensen and Walters, 2004); manual adjustments were then made during the process of tuning to time series data. The fitted vulnerabilities are presented in Table D.3.2. We first determined the critical vulnerability interactions in the model by use of an automated sensitivity analysis (a subroutine of the vulnerability optimization routine). We adjusted values for 75 out of 92 potential predator groups based on their interaction strengths; the 75 chosen were shown to have the greatest impacts on ecosystem dynamics. The remaining predator groups were allowed to retain default mixed control values (vulnerability = 2). The values for interaction vulnerabilities were set using Ecosim’s automatic optimization routine, and initially searching by predator (columns) so that each prey item receives the same value. Ecologically, this approach assumes that all prey are similarly vulnerable to a given predator. The assumption may be appropriate for the Raja Ampat suite of models because functional groups are partitioned in order to provide a highly detailed representation of reef associated species. If we assume that reef associated species generally rely on reef structure as a refuge from predators, this default assumption will be applicable to a large number of predator functional groups; all except a few highly specialized groups that employ distinctive hunting methods. As tuning continued through manipulation of the catch (Table D.2.1) and diet matrix (Table D.3.1), new optimal vulnerability values were determined for individual interactions. The automated routine was again used for this. However, the automated routine is designed to minimize data residuals between observed and predicted time series of catch and biomass. Often, a subjective improvement in the data fit is not accompanied by a reduction in residuals due to the fact that we have incomplete knowledge of ecosystem trends. A simple data fitting criterion, to reduce the sum of squares residuals between predicted dynamics and available time series, is usually not sufficient when there are large uncertainties surrounding time series information, as in the present case. We therefore manipulated vulnerability parameters manually to affect the shape of specific predation mortality trends.  Mediation functions A mediation function as used by Ecosim represents a non-trophic interaction in which the vulnerability of a given prey towards a given predator is affected by the biomass of a third mediating group. The mediation routine is used to represent protection and facilitation effects in the ecosystem (Christensen et al., 2004), and can capture key animal behaviours. Some applications of mediation functions in EwE are described by Okey et al. (2004) (sea floor shading by plankton blooms) and Cox et al. (2002) (tunas mediating forage fish mortality caused by birds). The preliminary Raja Ampat models of Ainsworth et al. (2007) applied four types of mediation functions to various ecosystem interactions. We use similar relationships here. The first function represents the facilitation effect that tuna can have in corralling small pelagics to the surface. When the mediating tuna groups (skipjack and other tuna) are in high abundance, the vulnerability of small pelagic fish groups (juv/ad small pelagic, juv/ad anchovy) to sea birds increases as sea birds forage more effectively. The second mediation function represents the protection offered by reef building corals on juvenile and subadult reef fish groups and octopus. With this mediation function, a high biomass of reef building corals  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  31  (hermatypic scleractinian corals) reduces the vulnerability of prey groups to all their predators. The third mediation function represents the positive effect that cleaner wrasse have on reef associated fish. We assume that the symbiotic grooming relationship improves the health of the client fish and provides them with a lower vulnerability to all their predators. The fourth mediation effect represents the protection offered by sea grass and mangroves to juvenile reef fish (juv. grouper/snapper) and shrimp (penaeid shrimps, other shrimps and prawns). We assume these prey are protected somewhat from all their predators. The shapes of the mediation functions are shown in Fig. 2.6; Table 2.11 shows the functional group assignments.  2  1  2)  Vulnerability  1)  Vulnerability  2  0  0 0  1 2 Mediating group biomass  0  1  4)  0  Vulnerability  Vulnerability  1 2 Mediating group biomass  2  2  3)  1  1  0 0  1 2 Mediating group biomass  0  1 2 Mediating group biomass  Figure 2.6. Ecosim mediation functions. Vulnerability of prey versus mediating group biomass. 1.) Tuna facilitating small pelagic predation by birds; 2.) reef-building coral protection of reef fish and invertebrates; 3.) cleaner wrasse symbiosis with large reef associated fish; 4.) sea grass and mangrove protection of juvenile reef fish. X and Y axes are relative to model baseline values.  The first and second mediation functions represent major behavioural effects in which the vulnerability of the prey group can be reduced close to 1 during periods of low / high abundance of the facilitating / protecting functional group. The vulnerability to predators can increase up to two times the baseline model value during periods in which the biomass of mediating groups is unfavourable for the prey. The second and third mediation functions represent minor behavioural effects in which the vulnerability of the prey can increase to 1.5 times the baseline value, or decrease to 0.5 times the baseline value. All mediation functions are linear, so that vulnerabilities increase or decrease linearly with the biomass of mediating groups. We used this simple assumption because the true relationships that govern mediation effects are likely to be complex, highly variable between functional groups, dependent on the baseline model, and difficult to parameterize empirically. The simplifying assumption can provide only a rough approximation to the true relationships occurring in the ecosystem because the ecological effects of a changing vulnerability term in the model are not linear throughout its potential range of values (1 to infinity). An increase of 10%, for example, will have a greater influence on system dynamics for a donor controlled  32  Chapter 1 Ecosystem Simulation Models of Raja Ampat  interaction (e.g., 1 to 1.1) than for a predator controlled interaction (e.g., 10000 to 11000). Until recently, there was a limitation in the mediation routine such that each predator-prey interaction could be governed by only one mediation function. Modellers were forced to choose only the most influential effects for any given predator-prey interaction. They could not, for example, model the protection that coral reefs impart on reef fish, while simultaneously representing the advantage conferred on them by cleaner wrasse. However, EwE Version 5 (revision of May 2007) has removed this limitation, and we can now represent multiple mediation effects on a single feeding interaction. Table 2.11. Mediation functions. Prey group Ad. groupers Sub. groupers Juv. groupers Ad. snappers Sub. snappers Juv. snappers Ad. Napoleon wrasse Sub. Napoleon wrasse Juv. Napoleon wrasse Ad. coral trout Juv. coral trout Ad. small pelagic Juv. small pelagic Ad. large reef assoc. Juv. large reef assoc. Ad. medium reef assoc. Juv. medium reef assoc. Ad. small reef assoc.  Mediation # 2,3 2 2,4 2,3 2 2,4 2,3 2 2,4 2,3 2,4 1 1 2,3 2,4 2,3 2,4 2  Prey group Juv. small reef assoc. Ad. large planktivore Juv. large planktivore Ad. small planktivore Juv. small planktivore Ad. anchovy Juv. anchovy Ad. macro algal browsing Juv. macro algal browsing Ad. eroding grazers Juv. eroding grazers Ad. scraping grazers Juv. scraping grazers Penaeid shrimps Shrimps and prawns Octopus Small crabs  Mediation # 2,4 2,3 2,4 2 2,4 1 1 2,3 2,4 2,3 2,4 2,3 2,4 4 4 2 2  Primary production forcing We use an automated routine in Ecosim to determine the primary production anomaly pattern that will minimize the discrepancy between the predicted biomass trajectories of functional groups from 19902005 and the observed catch and relative biomass estimates, based on governmental statistics. The production forcing routine, as integrated into Ecosim, adjusts the search rate of subject functional groups and so indirectly increases or decreases the annual production rate versus baseline24. A production forcing trend can be applied to any functional group to represent the affects of climate fluctuation on primary or secondary production (EwE production forcing: Christensen et al. 2004). By applying it to the phytoplankton functional group, we assume that fluctuations in primary production can cascade up the food web and affect the abundance of higher order species (Beamish, 1995; McFarlane et al., 2000). Ainsworth et al. (2007) did a similar search for an environmental production anomaly trend for Raja Ampat using an arbitrary number of spline points to smooth the resulting climate anomaly. They then rescaled and re-entered the production modifier trend into the 1990 Raja Ampat model so that the predicted annual phytoplankton biomass variability from simulations matched the observed variability from SeaWifs satellite primary production data (SeaWiFS, 2007. NASA Goddard Space Flight Center. Online resource. URL: http://oceancolor.gsfc.nasa.gov/SeaWiFS/). Here we use a 4.2% coefficient of variation (CV) in phytoplankton biomass. A spline point is a function used by Ecosim’s production anomaly search routine to smooth the resulting annual production anomaly trend. The routine uses a cubic spline method optimized with a nonlinear Levenberg-Marquardt search  24  Villy Christensen. UBC Fisheries Centre, 2202 Main Mall, Vancouver BC, personal communication.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  0.12  92  0.10  90 88  0.08  86 0.06 84 0.04  82  0.02  Sum of squares  We use this ‘moving window’ approach so that the coefficient of variation is not biased by directional biomass change, as may be caused by fishery depletions for example; instead, random environmental fluctuations are the main cause of interannual variation. The CV is based on data from the years 1998-2002. In the Sea Around Us project (2006) dataset, primary production is estimated from ocean colour; we assume that the trend is representative of our phytoplankton functional group biomass. Ainsworth et al. (2007) comment on the assumptions and caveats associated with this use of ocean colour data.  Coefficient of variation  algorithm (Press et al., 1995). The expected CV represents an average for all cells listed in the Sea Around Us project (2006) database in our study area, and it represents the average variation of each 5 year period between 1990-2005.  33  80  0.00  78 3  4  5  6  7  8  9  10  11 z ero  Spine points  Figure 2.7. Primary production anomaly CV. Additional spline points results in more variable phytoplankton biomass in the 1990-2006 dynamic simulation, and reduced residuals (sum of squares) versus observations. ‘Zero’ spline points represents no smoothing. Satellite data indicates an annual 4.7% CV for Raja Ampat. Using 8 spline points reproduces the appropriate level of production variability.  Time series reconstruction Using fitted vulnerabilities, mediation functions and primary production forcing in place we produce the best-fit to time series data in Fig. E.1.1. These figures compare the dynamic time series predictions by Ecosim and the empirically observed estimate of time series relative biomass derived from CPUE data (Ainsworth et al., 2007). Presented for comparison with the time series predictions are the 2005 model biomasses for functional groups as estimated in this report, along with confidence intervals representing an approximate ranking of data quality. The biomass values for all groups in the 2005 Raja Ampat model are presented in Table D.1.1. The confidence intervals are based on the default coefficients of variation used by the data pedigree routine in Ecosim as a ranking of data quality. These are: sampling based, high precision (c.v. = 10%); sampling based, low precision (c.v. = 30%); indirect method (c.v. = 50%); other method (c.v. = 80%). The biomass values for many functional groups are set in this report based on reef health monitoring dive transect studies; these received high data quality rankings (1-3). The top ranking of data quality (c.v. 10%) is reserved only for the grouper and snapper functional groups because all of the species in those groups (all Serranidae and Lutjanidae species, respectively) were specifically noted by divers. For aggregated functional groups like large reef associated fish, a fewer relative number of species were specifically recorded. These groups receive a lower ranking of data quality (4-5). Most other data is taken from outside the Raja Ampat ecosystem, or estimated by Ecopath (see Ainsworth et al., 2007 for EwE group descriptions). In most cases, the 1990 model, when driven forward 15 years using historical fishing effort trends, mediation and forcing functions, produces a reasonable representation of the 2005 ecosystem. Although there are discrepancies between the end-state of the 1990-2005 simulation and the 2005 model, the discrepancies tend to occur within data-poor groups such as those representing many species (highly aggregated groups), and those representing poorly studied organisms; especially, basal species and unexploited invertebrates; these have large confidence intervals in Fig. E.1.1. In the case of aggregated groups we can, at best, know the biomass history for only a small fraction of the member species. The default assumption we have used, that other species in those groups have exhibited similar population dynamics over the last 15 years as the better-known species, provides us with only a rough idea of the aggregate biomass trends. Both the time series trends and the 2005 biomass values for these groups are therefore approximate. In some cases, the simulation biomass, which is constrained by the system’s thermodynamic requirements, is probably more accurate than the relative biomass or catch information used to tune the model.  34  Chapter 1 Ecosystem Simulation Models of Raja Ampat  The dynamics of most well-studied and highly commercial functional groups, such as groupers, snappers and tuna, are adequately represented in simulations inasmuch as the CPUE trends are accurate reflections of their relative biomass. The dynamics of these groups tend to be dominated by the effects of fisheries. Therefore, the observed trend from CPUE data can be recreated with some accuracy using fishing drivers as the principle mortality source. For groups that are less exploited by fisheries, population dynamics are determined by a combination of fishing mortality and natural mortality. The latter is more difficult to estimate because it represents the combined effect of many predators, and each diet interaction carries uncertainty. Biomass dynamics of immature and sub-adult life history stanzas tend to be poorly predicted, particularly for groupers and snappers, which each have here 3 life history stages. The biomass trajectories for these groups do not result in an end-state 2005 configuration that resembles the 2005 model, but this is partly due to a modelling limitation. The multi-stanza routine in Ecosim assumes a static (equilibrium) age structure. However, we intentionally adjusted the production rate of the age stanzas in the 2005 model relative to the 1990 model in order to represent a shift in the assemblage composition towards younger individuals for some exploited groups (Table D.4.1). It is therefore difficult in principle to fit both the adults and sub-adults simultaneously without age-specific biomass trends. Moreover, we cannot accomplish the life history parameter shift gradually using the dynamic facilities of Ecosim since we have no direct control over the age-structure short of forcing population biomass. We have chosen to focus on the adult stanzas in tuning, preferring to develop dynamics for this group that match the available CPUE trends, while allowing sub-adults and juvenile groups to take whatever biomass values are predicted by the stable age structure.  EQUILIBRIUM ANALYSIS An equilibrium analysis provides an invaluable way of validating EwE model behaviour. It is second in importance only to fitting the dynamics against time series data. In an equilibrium analysis, we are interested in determining the absolute level of biomass that each functional group in the ecosystem assumes at long time scales under a given fishing pattern and level of fishing intensity, and the corresponding amount of catch. By holding the fishing level constant on all functional groups except our subject group, we can map out the estimated population response at a variety of fishing intensities. This method allows us to quantify and represent the exploitation status of stocks, and so to compare the behaviour of the model with our a priori understanding of the ecosystem. The equilibrium analysis that is conducted for an ecosystem model produces outputs analogous to biomass dynamic models commonly used in single-species fisheries management. The biomass of an exploited group will usually be highest under zero fishing effort (the catch then will also be zero); this biomass level is referred as B0, or unfished biomass. As fishing intensity increases, catch on the subject functional group will increase to a maximum, which is called Maximum Sustainable Yield (MSY: Russell, 1931; Graham, 1935). When fisheries take exactly this amount, the biomass at maximum sustainable yield (BMSY) can be maintained at equilibrium (in principle, with caveats). However, when catches exceed this amount overfishing occurs. Biomass is removed from the stock faster than the replenishment rate from growth and reproduction, and the population assumes depressed biomass equilibrium; catches will be sub-optimal. Other useful fishery indicators can be determined through the equilibrium catch and biomass curves including the precautionary fisheries management objective F0.1. F0.1 represents the point on the yield per recruit curve at which the slope of the line tangential to the curve is equal to one-tenth the slope of a line tangential to the curve at the origin (Gulland and Boerema, 1973). F0.1 is always lower than FMSY, and it has been suggested as a safer target for management. An equilibrium analysis using an ecosystem model offers a major advantage over single species methods because it accounts for species interactions. Even though an ecosystem model represents a greatly simplified abstract of the true ecosystem, the number of trophic and non-trophic interactions increases exponentially with the number of functional groups. These interactions can combine in unexpected ways to greatly affect stock dynamics. The multispecies surplus production curves can differ drastically compared to a similar single species estimate, and the sources of these discrepancies are important to consider in an EBFM framework. We perform this comprehensive review of model behaviour here and  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  35  present the results in a series of equilibrium catch and biomass curves for exploited species. As with analogous single species methods, the equilibrium analysis relies on the assumption of deterministic population behaviour in growth, recruitment and mortality, and so is subject to similar criticisms (e.g., Larkin, 1977; Punt and Smith, 2001). Climate variation, for example, can only reduce the estimate of safe harvest limits. Ecosim contains an automated routine to establish the equilibrium catch and biomass curves. However there is a technical problem with the routine that will prevent the curves from being comparable to analogous single-species procedures. This problem is accentuated in models that use multiple ontogenetic stanzas, like the present Raja Ampat models. The problem is that the automated routine can only increment the fishing mortality on a single age stanza. If all of the fishery catch is directed to a single stanza, the adult group for example, this limitation is not an issue. But if, however, there is significant fishing on other age classes, such as the sub-adult or immature stanzas, the automated routine will assume a constant (baseline) fishing pressure on these stanzas. The result is that the adult group will seem unrealistically resilient to the effects of fishing as the younger age classes, unaffected by all but a baseline level of fishing pressure, continue to recruit into the adult stanza. The catch curve for the adult group in this case will be shifted towards the right (e.g., see Figure E.2.1), indicating that it can support a high level of fishing mortality. In reality, however, an increase in fishing mortality on the adult group will usually be accompanied by an increase of fishing mortality on the younger age classes due to the unselective nature of fishing gear. Hence, because the Raja Ampat models contain many multi-stanza groups whose sub-adult and juvenile age stanzas are subject to fishing, we opted to avoid the use of the automated equilibrium routine and instead perform the calculations manually – incrementing fishing mortality on all fished age classes simultaneously and allowing the populations to come to their fishery-induced equilibrium biomass level. Although the procedure is far more time consuming than the automated method, it generates more realistic equilibrium curves that better reflect the exploitation status of stocks.  RESULTS RECONSTRUCTED HISTORICAL BIOMASS FROM LEK DATA The following results are supplemental to the findings in Ainsworth et al. (2008). The unprocessed responses obtained from the LEK interviews are presented in Fig. B.1.1 for the 44 species groups tested. The LEK trend for the periods 1970, 1980, 1990 and 2000 were determined by Ainsworth et al. (2008) for all species groups based on the output of the fuzzy logic routine; these are presented in Fig. B.2.1. Using the scaling factor from the CPUE data set relative to 1990 and 2000, those authors back-calculated the relative change from 1970 to present. They presented a selection of the outputs, but the complete results are in this document (Fig. B.2.2). By assuming an absolute biomass in 2000 which is based on the Ecopath estimates of Ainsworth et al. (2006), Ainsworth et al. (2008) determined the biomass for functional groups in 1970. This value was assumed to be similar to B0 (Fig. 3.1; reproduced from Ainsworth et al., 2008). The other unexploited biomass estimates in Fig. 3.1 (represented by the lower trend line) are determined using the models presented in this report. Those values correspond to the left-most biomass value in the equilibrium graphs in Fig. E.2.1; they represent the biomass value of the functional group at equilibrium as established after a 20 year simulation from 2005 to 2025 under zero fishing mortality.  REEF HEALTH MONITORING Results from the reef health monitoring study are expected to be published by TNC in 200825. Results from Waigeo Is. are also forthcoming from CI26. We provide a preliminary summary of the data for Kofiau Is. and Misool Is. as follows to support the current biomass density calculations for the EwE models. The biomass calculated for these fish families was converted to represent EwE functional groups by the procedure detailed in the methods section; this yielded the final biomass values per EwE functional group 25 Contact Andreas Muljadi (Kofiau Island;amuljadi@tnc.org) and Mohammad Syakir (SE Misool Is.; msyakir@tnc.org) at TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. 26 Contact: M. Erdi Lazuardi. CI. Jl Arfak No. 45. Sorong, Papua, Indonesia 98413. E-mail: erdi@conservation.or.id  36  Chapter 1 Ecosystem Simulation Models of Raja Ampat  in Table D.1.1. Note that many of the biomass density estimates were modified by an arbitrary scaling factor during the process of model balancing and tuning to data (see Section 2.2); the scaling factors are also reported in Table D.1.1. The functional group biomasses that are set based on the reef health monitoring data are demarked by reference #1 in that table; Table D.1.2 describes the methods used for other functional groups.  Herbivorous fish Biomass densities for herbivorous fish families in Kofiau are presented from the reef health monitoring data in Fig. 3.2 with average body weight per individual; Fig. 3.3 shows Misool results.  3  25  Body weight (kg)  -2  Biomass (t·km )  30  20 15 10 5 0 Scaridae  Acanthuridae  2  1  0  Siganidae  Scaridae  Acanthuridae  Siganidae  Figure 3.2. Herbivorous fish family biomass and individual body weight for Kofiau. Data from reef health monitoring. Mean values shown for 59 dives; error bars show 1 SD. Source: reef health monitoring study (Andreas Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413, unpublished data).  3  40  Body weight (kg)  -2  Biomass (t·km )  50  30 20 10 0 Scaridae  Acanthuridae  Siganidae  2  1  0 Scaridae  Acanthuridae  Siganidae  Figure 3.3. Herbivorous fish family biomass and individual body weight for Misool. Data from reef health monitoring. Mean values shown for 182 dives; error bars show 1 SD. Source: reef health monitoring study (Mohammad Syakir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413, unpublished data).  Piscivorous fish The average biomass density for piscivorous fish in Kofiau Is. sites is reported by family in Fig. 3.4 along with individual body weights by family. Fig. 3.5 shows the results for SE Misool Is.. Fig. 3.6 shows the biomass of Kofiau Is. piscivorous fish by species and Fig. 3.7 shows the results for SE Misool Is..  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  37  60  5  50  30 20 10  3 2 1  Scombridae  Carangidae  Lutjanidae  Scombridae  Sphyraenidae  Carangidae  Serrandiae  Lutjanidae  S phyraenidae  0  0  S errandiae  Biomass (t·km  -2  Body weight (kg)  )  4  40  5  200  4 Body weight (kg)  250  -2  150 100 50 0  3 2 1  Labridae  Sphyraenidae  Scombridae  Lutjanidae  Serranidae  Labridae  Lutjanidae  Carcharhinidae  Carangidae  Scombridae  Serranidae  Sphyraenidae  0 Carangidae  Biomass (t·km )  Figure 3.4. Biomass and individual weight of piscivorous fish at Kofiau Island by family. Mean values shown for 26 dives; error bars show 1 SD. Total number of fish observed: Lutjanidae (152), Serranidae (75), Carangidae (27), Sphyraenidae (1) and Scombridae (2). Source: reef health monitoring study (Andreas Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413., unpublished data).  Figure 3.5. Biomass and individual weight of piscivorous fish at SE Misool by family. Mean values shown for 91 dives; error bars show 1 SD. Total number of fish observed: Sphyraenidae (12), Serranidae (1082), Scombridae (443), Carangidae (1878), Carcharhinidae (1), Lutjanidae (28), Labridae (2). Source: reef health monitoring study (Mohammad Syakir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413., unpublished data).  38  Chapter 1 Ecosystem Simulation Models of Raja Ampat -2  Biomass (t·km ) 0  2  4  6  8  Lutjanus bohar Aprion virescens Variola albimarginata Lutjanus rivulatus Plectropomus leopardus Variola louti Gnathanodon speciosus Lutjanus argentimaculatus Gracila albimarginata Epinephelus maculatus Epinephelus polyphek adion Plectropomus areolatus Elegatis bipinnulatus Epinephelus fuscoguttatus Scomberomorus commerson Plectropomus laevis Caranx melampygus Epinephelus coioides Plectropomus oligocanthus Plectropomus maculatus Caranx sexfasciatus  Figure 3.6. Biomass density of Kofiau piscivorous fish species. Mean values shown for 26 dives; error bars show 1 SD. Source: reef health monitoring study (Andreas Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413, unpublished data). -2  Biomass (t·km ) 0  5  10  15  20  25  Caranx melampygus Sphyraena barracuda Caranx sexfasciatus Lutjanus rivulatus Gnathanodon speciosus Plectropomus leopardus Carcharhinus melanopterus Plectropomus areolatus Plectropomus maculatus Lutjanus bohar Cheilinus undulatus Caranx ignobilis Lutjanus argentimaculatus Scomberomorus commerson Plectropomus oligocanthus Gracila albimarginata Variola louti Variola albimarginata Epinephelus fuscoguttatus Gymnosarda unicolor Epinephelus coioides Epinephelus polyphek adion Plectropomus laevis Epinephelus lanceolatus Epinephelus maculatus Epinephelus tuk ula Epinephelus malabaricus Epinephelus caruleopunctatus  Figure 3.7. Biomass density of SE Misool piscivorous fish species. Mean values shown for 91 dives; error bars show 1 SD. Source: reef health monitoring study (M. Syakir. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413, unpublished data).  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  39  GUT CONTENT ANALYSIS Stomach sample results The diet information for functional groups as determined by the stomach sampling program is presented in pie charts in Fig. 3.8. Groupers  Snappers  Skipjack tuna  Other tuna  Mackerel  Coral trout  Large sharks  Small sharks  Rays  Butterflyfish  Large pelagic  Medium pelagic  Small pelagic  Small demersal  Large planktivore  Large planktivore  Large reef associated Medium reef associated  Deepwater fish  Detrivore fish  Figure 3.8. Diet for EwE functional groups estimated from family-level gut contents. Diet items contributing less than 10% are included in ‘Others’ category. Source: BHS EBM stomach sampling study (C. Rotinsulu. CI. Jl. Gunung Arfak. 45. Sorong, Papua, Indonesia, unpublished data).  Table C.2.1 shows the stomach sample data where prey groups have been assigned into their appropriate EwE groups without any further data processing (e.g., polychaetes are assigned to infaunal invertebrates). Entries in the Y-axis of this table represent a straight-forward summation of the stomach contents into the  40  Chapter 1 Ecosystem Simulation Models of Raja Ampat  appropriate categories. Table C.2.2 presents the results of the stomach sampling data for use in the models; predator fish families have been aggregated into EwE functional groups according to conversion ratios in Table C.1.1 (see Methods). Table C.2.3 provides the diet algorithm results from Ainsworth et al. (2007). These original data were subsequently modified by Ainsworth et al. (2007) in the process of balancing and tuning the model, but we have opted to compare the stomach sampling results with the original diet algorithm output. Once the predators and prey items are aggregated into EwE functional groups, 66% of feeding interactions identified by the stomach sampling program are successfully predicted by the diet allocation algorithm of Ainsworth et al. (2007). For review, this algorithm processes FishBase diet information (Froese and Pauly, 2007) at the species and family level into a form more applicable to the specific functional group structure used in the present model. The remaining 34% are mainly minor interactions (Figure 3.9). Of the predator-prey interactions that are absent from the diet algorithm results, but identified by stomach sampling, only a small number (4.2%) constitute major diet components (i.e., consisting of 25% or more of a predator’s diet). This suggests that the diet algorithm of Ainsworth et al. (2007) performed adequately in predicting the major diet interactions in the ecosystem. In order to compare the output of the diet algorithm of Ainsworth et al. (2007) with the stomach sampling data we considered 16 the rank order of all of the diet 14 interactions (per predator) in both data sets (i.e., Tables C.2.2 and C.2.3 for 12 stomach samples and diet algorithm 10 respectively). Each prey item for a given predator was assigned a rank, where large 8 diet items correspond to a low rank, small 6 diet items correspond to a high rank and the rank of the smallest diet item is equal 4 to the total number of prey items. The 2 rank order direction is not important to the method. The squared difference 0 between the ranks of each prey item was 5 10 15 20 25 30 35 40 45 50 taken as a measure of the discrepancy Percent diet composition absent from predator diet between the data sets for that interaction. Using this technique we categorized the Figure 3.9. Feeding interactions identified by stomach sampling discrepancies among all predator-prey and not diet algorithm. The majority of interactions missed by the interactions common to both data sets, diet allocation algorithm are minor interactions, constituting less than 5 or 10 % of the predator’s diet. Only a small fraction of and the top 25 percentile of these major interactions were missed by the diet algorithm of discrepancies were said to represent Ainsworth et al. (2007). critical disagreement. These interactions were considered for revision in the Raja Ampat models. Table 2.9 shows the top 25 percentile of discrepancies using this rank squared difference method. Percentage of interactions  18  Among these critical disagreements, we consider whether the stomach sampling data conflicted with the balanced diet value of the preliminary models (Ainsworth et al., 2007), the diet algorithm, or both. If either the algorithm agrees with the stomach data, or the balanced model agrees with the stomach data, then the diet composition data point remained unchanged in the revised models. The reasoning is as follows. If the stomach data agrees with the algorithm but disagrees with the model, then we assume that the diet algorithm successfully predicted the interaction and the change made by Ainsworth et al. (2007) during the process of balance and tuning the model amounts to a necessary loss of agreement in order to achieve mass-balance and improve the fit to time series data. If the stomach data agrees with the model but disagrees with the algorithm, then we assume that the process of balancing and tuning the model has corrected an errant diet point predicted by the diet algorithm. Interestingly, far more interactions fall into the latter category, indicating that the process of balancing and tuning has improved the diet algorithm results (see Table 2.9). If both the algorithm and the balanced model conflict with the stomach samples, then we change this value in the present revised version of the model. These data points are marked by grey cells in Table 2.9.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  41  Table C.2.4 and Table C.2.5 show the direction and absolute magnitude of the disagreement that the stomach sampling data shows compared to the balanced preliminary model and diet algorithm, respectively. For the interactions marked for revision, we adopted the stomach sample percent composition for that prey item in the model (Table 3.1), adjusting the other prey items with a straight scaling factor so that the total diet of a predator remains at unity. In some cases, the subsequent balancing and tuning of the revised models necessitated the changing of these parameters slightly to yield the final diet matrix for the 2005 Raja Ampat model in Table D.3.1. All of the models, including the 1990 Raja Ampat model and the sub-area models were assigned an identical diet matrix before balancing and tuning. Differences between these matrices should reflect the varying biomass densities of functional groups in the various habitat maps. Table 3.1. Diet interactions changed to match stomach sampling compositions. Predator  Ad. large reef assoc.  -  -  Adult medium reef assoc. 0.0487  Ad. medium reef assoc.  -  -  -  Prey  Ad. small reef assoc.  Snappers  Adult large reef assoc.  Adult large planktivore  Deepwater fish  -  -  -  0.1248  -  -  -  -  0.1872  Ad. macro algal browsing  0.0844  -  0.0244  0.0159  -  Ad. eroding grazers  0.0844  0.0194  0.0244  -  -  Hermatypic corals  -  0.1180  0.0994  -  -  Soft corals  -  0.0590  -  -  -  Shrimps and prawns  0.0131  -  -  -  0.0123  Squid  -  -  -  0.0006  -  Epifaunal det. inverts.  -  0.0009  -  -  0.0027  Epifaunal carn. inverts  -  0.0009  0.0011  -  0.0027  Infaunal inverts.  -  -  0.0026  0.0058  0.0066  Macro algae  -  0.0046  0.0007  -  -  Sea grass  -  -  0.0004  0.0045  -  42  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Relative biomass  12 10 Range of likely decline  8 6 4  Ad. anchovy  Other tuna  Skipjack tuna  Ad. small demersal  Ad. small reef assoc.  Ad. large demersal  Ad. medium reef assoc.  Ad. snappers  Ad. deepwater fish  Ad. groupers  Ad. large reef assoc.  Penaeid shrimps  Shrimps and prawns  Ad. medium pelagic  Ad. large planktivore  Mackerel  Ad. large pelagic  Sea cucumbers  Ad. small sharks  Ad. small pelagic  Lobsters  Ad. large sharks  Large crabs  Small crabs  Bivalves  Octopus  0  Epifaunal carn. inverts  2  Functional group Figure 3.10. Likely range of group biomass depletion from unexploited levels to the present day. The upper bound shows the biomass decline suggested from LEK data; 1970 period is assumed similar to unexploited biomass, B0, and 2005 period indicates present day; the lower bound shows the biomass decline suggested by the EwE equilibrium analysis. LEK data indicates more severe declines from the unexploited biomass than equilibrium predictions by the model would suggest. Shaded area represents the range of likely decline. Reproduced from Ainsworth et al. (2008).  ECOSIM ANALYSIS Equilibrium analysis The equilibrium analysis is conducted for exploited functional groups in (Fig. E.2.1). These graphs show the level of catch and biomass that can be expected at biomass equilibrium under various levels of fishing mortality. The calculated fishing mortalities at FMSY, F0.1 and F2005 are presented in summary in Table 3.2. The right-most column in this table indicates the current level of exploitation with respect to the level of fishing that produces MSY. Functional groups approaching FMSY can be considered to be fully exploited. Functional groups at FMSY are likely to be overexploited once environmental variability and the inelasticity of fishing capital (and therefore fishing effort) are finally considered. Results suggest that some functional groups are overexploited.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  43  Table 3.2. Equilibrium analysis results. Various fishing effort indicators and the corresponding amount of catch at biomass equilibrium. Catch at MSY  FMSY  Catch at F0.1  F0.1  Catch in 2005  F2005  F2005/FMSY  (kg—km-2)  (yr-1)  (kg—km-2)  (yr-1)  (kg—km-2)  (yr-1)  (-)  Groupers  114.5  0.376  114.5  0.365  87.7  0.188  0.50  Snappers  95.0  0.531  94.0  0.398  92.0  0.332  0.63  Napoleon wrasse  18.5  0.348  18.5  0.348  18.5  0.348  1.00  Coral trout  6.5  0.414  6.5  0.414  5.4  0.188  0.45  Large sharks  27.0  0.649  26.9  0.541  26.9  0.541  0.83  Small sharks  11.7  0.585  11.7  0.527  9.9  0.293  0.50  Butterflyfish  142.2  0.874  141.8  0.826  70.3  0.243  0.28  Large pelagic  50.7  0.734  50.7  0.734  50.7  0.734  1.00  Medium pelagic  12.1  0.649  12.1  0.649  10.7  0.405  0.63  Small pelagic  88.2  1.058  88.2  1.058  87.1  0.882  0.83  Large reef associated  770.9  0.232  770.9  0.232  616.9  0.116  0.50  Medium reef associated  286.5  0.275  284.4  0.344  208.5  0.138  0.50  Small reef associated  95.0  1.541  95.0  1.541  49.6  0.426  0.28  Large demersal  57.0  0.304  56.2  0.334  57.0  0.304  1.00  Small demersal  112.9  0.897  112.9  0.897  85.2  0.359  0.40  Large planktivore  439.4  0.763  439.4  0.763  344.4  0.381  0.50  Small planktivore  347.6  1.840  347.6  1.840  170.5  0.460  0.25  Anchovy  626.7  0.753  626.7  0.753  217.5  0.237  0.31  Deepwater fish  211.3  2.482  193.9  0.886  147.6  0.355  0.14  Macro algal browsing  110.3  1.000  110.3  1.000  110.3  1.000  1.00  Eroding grazers  165.5  1.439  165.5  1.439  165.5  1.439  1.00  Skipjack tuna  579.1  0.746  545.6  0.522  545.6  0.522  0.70  Other tuna  36.9  0.090  36.8  0.084  35.5  0.095  1.06  Functional group  44  Chapter 1 Ecosystem Simulation Models of Raja Ampat  DISCUSSION FISH BIOMASS We have used the reef health monitoring assessments in this volume to estimate the biomass of reef fish for the models. However there are number of uncertainties associated with the procedure. Sites were selected randomly, and the scale of the sampling program was large compared to other previous TNC exercises in Komodo, and in other parts of Raja Ampat (e.g., Waigeo Is. in COREMAP 2002). Nevertheless the results of the study will be highly dependent on the local oceanography and biogeography of the reef structure. This introduces a good deal of uncertainty once we scale the results up to represent the total area of Raja Ampat. In effect we have assumed that population structure on the reef system around Kofiau and Misool Islands are similar to other parts of Raja Ampat. There is also some uncertainty as to whether fish are counted only once. In the case of sedentary and territorial reef fish like groupers (Serranidae) this source of error will be minimized. However, the uncertainty is potentially a large one for species such as snappers (Lutjanidae) which are mobile and tend to school. A single incidence of a large school can render the information unrepresentative of the area as a whole, so there are observational uncertainties. Cryptic species too may be underestimated in the reef health monitoring data, and we did not account for this. Unfortunately, we were not able to make use of a large part of the reef health monitoring data, the data from Waigeo Is., because it was not available in time for this study.  REQUESTED EBFM ANALYSES USING EWE MODELS A workshop at the TNC office in Sanur, Bali held July 16-17, 2007 with TNC, CI, WWF, UBC and Packard staff provided very clear EBFM objectives for the EwE modelling study. In addition, we received specific requests for analyses by the Raja Ampat Regency fisheries bureau27. Hence, the research questions investigated by the trophic modelling are as follows: •  • • •  • • • •  What are the likely ecosystem effects of changes in the anchovy fishery under the following management scenarios? o Anchovy fishery is completely removed from Raja Ampat; o Limited anchovy fishery is allowed; o Anchovy fishery continues to increase in size. What are the likely effects of restricting the commercial exploitation of groupers? What are the likely effects of excluding all net fisheries for reef fish in Raja Ampat? What are the likely effects of blast fishing under the following scenarios? o Status quo; o Increase. What are the likely effects of an increase in the tuna fishery? What is the unfished biomass estimated by the model for Hawksbill turtles? What might the unexploited ecosystem of Raja Ampat have looked like? Under an optimal fishing policy, how might an increase in fishing levels affect the ecology and economy of Raja Ampat fisheries? (Alternatively, what economic benefits must be sacrificed to preserve an acceptable level of biodiversity?)  Some of these questions are addressed in a recent article by Ainsworth et al. (2008b), while a number of other articles have been submitted to peer-reviewed journals, or are in preparation, which attempt to answer other important questions for EBFM. Please see Appendix F for article titles, abstracts and journal to which they have been prepared or submitted.  Becky Rahawarin. Kepala Dinas Perikanan dan Kelautan, Raja Ampat. Jl. A. Yani, Kuda laut, Sorong, Papua, personal communication.  27  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  45  FUTURE WORK The analyses that were conducted in this contribution constitute a first attempt at understanding the influence of fisheries in an ecosystem context. However, there are additional scientific questions that could be addressed by use of the present suite of models in a follow-up project. There are also some questions that could be more fully investigated by use of a different modelling system. Aquaculture, particularly mariculture, may have the potential in Eastern Indonesia for significant development (Priyono and Sumiono 1997). That is the current belief of the Raja Ampat fisheries office, and the Bureau intends to pursue expansion of aquaculture industries. They are interested in increasing the amount of pearl farming in Raja Ampat, for example, in the Kofiau-Boo Island group (Fig. 4.1), which would add to the already established industry in SW Misool Island. They also intend to facilitate the development of grouper grow-out operations in the south of Waigeo Is. Expanding the aquaculture industry could improve economic options for rural communities and companies in Raja Ampat, but there is concern that fisheries for Figure 4.1. Pearl farming operation in the Kofiau Island grouper seed for grow-out operations could group. Photo: Cam Ainsworth. threaten stocks28, as has happened elsewhere in South East Asia (Liu and Sadovy, in press). Depletion of grouper has already been seen in Raja Ampat (Ainsworth et al. 2008) as a result of increased fishing, and there has been an apparent disappearance of many grouper spawning aggregations29. So grouper grow out operations might further prejudice already depleted stocks. To accurately determine the exploitation status of groupers would require further study, and the comparison of outcomes from several modeling approaches would provide a more robust analysis than the work here can offer alone. Despite the contributions made by the various studies in the BHS EBM project, Raja Ampat remains a data-poor area. One of the justifications for performing the type of ecosystem analysis attempted here is that we are guided to what may be a reasonable representation of the ecosystem by the thermodynamic constraints imposed by the better-understood parts of the ecosystem. There is of course a wide range of uncertainty associated with all of our estimates. And while more knowledge is certainly beneficial to fishery management and EBM, fully understanding the status and behaviour of the ecosystem will likely remain out of our grasp long after we have compromised the long-term productive potential of the ecosystem through the inaction of management. Instead, we are wise to adopt a precautionary approach to management, and one suggestion has been to implement marine protected areas as a hedge against our own uncertainty (Sumaila and Alder, 2001). The approach may be advisable in an area like Raja Ampat in particular, where compliance with community-based management approaches, especially area protection schemes, is forthcoming in reef areas close to settlements (Crawford et al., 2004). Subsequent studies using the models in this volume may provide further insight into the usefulness of area management schemes in Raja Ampat.  Becky Rahawarin. Kepala Dinas Perikanan dan Kelautan, Raja Ampat. Jl. A. Yani, Kuda laut, Sorong, Papua, personal communication. 29 Peter Mous. COREMAP II. Jl. Tebet Raya No. 91, Jakarta, Indonesia, personal communication; Coastal Rural Appraisal Study: J. Wilson. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228, e-mail: joanne_wilson@tnc.org. 28  46  Chapter 1 Ecosystem Simulation Models of Raja Ampat  CONCLUSIONS EXPLOITATION STATUS OF RAJA AMPAT REEF FISHERIES When we compare the finalized Raja Ampat models in this volume, which include the latest project information, with the preliminary models of Ainsworth et al. (2007), we note two major changes have affected the parameterization and lead to quite different biomass dynamics being portrayed. Biomass estimates for fish functional groups have generally, though not always, been revised upwards based on outputs from the reef health monitoring dive transect studies. Reef fish biomass density, at least for reefs investigated, seems higher in Kofiau and SE Misool Is. than other areas of Raja Ampat which previous studies have considered (e.g., COREMAP 2005), and on which the preliminary biomass values were based in Ainsworth et al. (2007). The other major change is that the IUU analysis, which was conducted using project data from the CI Socioeconomic Valuation report (Dohar and Anggraeni, 2007), personal communications from in-field experts and other literature sources identified in Varkey et al. (in prep.), reduced the total catch estimates used by Ainsworth et al. (2007) for many functional groups. These included preliminary ‘place-holder’ IUU estimates which were uncertain and based either on expert communications or set arbitrarily. The result from these two major changes is that some of the functional groups that were considered to be over exploited by Ainsworth et al. (2007) (e.g., large sharks, large demersal fish) now appear to be fully exploited in the revised models, while some groups that were previously considered to be fully exploited are now set as being under exploited (e.g., medium pelagic). Relatively fewer groups have had their exploitation status revised upwards, and these tend to include only lightly exploited species (e.g., small demersal fish, small planktivorous fish). These findings suggest that overfishing may not yet be the most serious threat facing most functional groups in the marine environment of Raja Ampat. Although there have certainly been serious depletions, highlighted for example by the conspicuous absence of previously identified grouper spawning aggregation sites30, depletions have thus far been localized in space and restricted in terms of the number of species affected. Depletion of the most valuable stocks of grouper, snapper, Napoleon wrasse, coral trout and other high value reef fish have likely had a disproportionately large impact on the profitability of commercial reef fisheries, but artisanal fisheries continue unabated for mixed species catches. It is the opinion of the local community members in Raja Ampat that overfishing is not the largest threat facing the marine ecosystem (Halim and Mous, 2006); they more often cite destructive fishing practices, and this lends support to our conclusion that Raja Ampat must still retain a high exploitable biomass of reef fish species. The concern is that with increased human habitation and developing infrastructure aimed at facilitating commercial trade, exploitation will continue to increase in the short term.  BHS EBM PROJECT The BHS EBM project has provided a rare and valuable opportunity to integrate field data collected directly in support of complex ecosystem models, and then to use that new information to address specific EBFM questions relevant to the region. Raja Ampat is relatively data-poor, especially when considering the large amount of initializing data required by an ecosystem model, so the new information collected by project is extremely useful. However, the cooperation of knowledgeable scientists in the BHS EBM project and from outside the project has made this holistic approach possible. Support from the community of marine researchers in Indonesia has been fostered by the excellent working relationship that TNC, CI and WWF share with stakeholders at the village level, in government and at academic institutes. The relationships they have built through cooperation and local involvement may prove to be one of the most useful outputs of this project towards eventually implementing an EBFM agenda. In fact, the rural communities of Raja Ampat have been very supportive of new initiatives to preserve the marine environment and implement EBFM measures (e.g., establishing new marine protected areas). Close ties to the marine environment and a tradition of stewardship among the rural people of Papua will lend support to the shared vision of EBFM in this valuable and biodiverse marine seascape. 30 Peter Mous. COREMAP II. Jl. Tebet Raya No. 91, Jakarta, Indonesia, personal communication; Coastal Rural Appraisal Study: J. Wilson. TNC-CTC. Jl Pengembak 2, Sanur, Bali, Indonesia, 80228., email: joanne_wilson@tnc.org.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  47  ACKNOWLEDGEMENTS We would like to give special thanks our research partners in Indonesia, especially Mark Erdmann, Peter Mous, Joanna Wilson, Kai Lee, Kristin Sherwood, Chris Rotinsulu, Dewa Gede Raka Wiadnya, Lida PetSoede, Reinhard Poat, Anton Suebu, Obed Lense, Adityo Setiawan, Jos Pet and others at TNC, CI and WWF for their valuable input and discussion on the models and the Raja Ampat ecosystem. We acknowledge Megan Bailey for discussions, especially regarding anchovy in the models, and for contribution of photos. We also acknowledge the assistance of Vicky Lam (UBC Fisheries Centre) with GIS materials.  REFERENCES CITED Ainsworth, C.H., Varkey, D., Pitcher, T.J., 2007. Ecosystem simulation models for the Bird’s Head Seascape, Papua, fitted to data. Pages 6–172 In: Pitcher, T.J., Ainsworth C.H. Bailey, M. (Eds.) 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Based on unpublished data from A. Muljadi. (2007) TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Length-weight relationships from FishBase (www.fishbase.org). Species name  Family  Lutjanus bohar  Lutjanidae  Aprion virescens  Lutjanidae  EwE group  Snappers Large planktiv.  Mean length Length N (TL; SD (cm) cm)  a  b  Area Average Mean Average covered Biomass Weight % dives biomass Biomas weight visibilit by density SD (g) spotted per dive s SD (g) (g) y (m) transect (t—km-2) (g) (m2)  45.8  13.0  59 0.015 3.077  1023  1567  38%  5905  7140  8.8  1860  3.175  44.9  6.3  71 0.008 3.134  1522  301  15%  3856  2778  11.7  1800  2.142  Variola albimarginata  Serrandiae  Groupers  31.5  8.5  22 0.021 3.004  421  566  50%  755  320  8.4  1669  0.453  Lutjanus rivulatus  Lutjanidae  Snappers  33.8  3.6  17 0.025 3.000  1023  335  27%  652  367  7.5  1543  0.423  Plectropomus leopardus  Serranidae  Coral trout  48.5  11.5  4  0.010 3.138  2314  1555  15%  356  228  5.3  1050  0.339  Variola louti  Serranidae  Groupers  36.4  9.5  14 0.015 3.024  976  837  31%  525  170  7.9  1663  0.316  Gnathanodon speciosus  Carangidae Large reef assoc.  35.3  3.8  12 0.043 2.843  968  295  8%  504  -  8.0  1600  0.315  Lutjanus argentimaculatus  Lutjanidae  Snappers  45.0  12.7  5  0.015 3.059  1023  1902  19%  410  213  8.6  1720  0.238  Gracila albimarginata  Serranidae  Groupers  36.4  3.3  10 0.021 3.004  1023  226  19%  394  214  8.4  1760  0.224  Epinephelus maculatus  Serranidae  Groupers  42.5  8.0  9  0.014 2.990  1101  642  12%  381  124  8.7  1733  0.220  Epinephelus polyphekadion Serranidae  Groupers  38.0  3.5  3  0.016 3.029  968  250  12%  112  24  6.0  1200  0.093  Plectropomus areolatus  Serranidae  Coral trout  42.7  6.4  3  0.021 3.004  1755  820  12%  202  41  11.7  2333  0.087  Elegatis bipinnulatus  Carangidae Large planktivore  40.0  0.0  10 0.018 2.580  251  -  4%  96  -  8.0  1600  0.060  Epinephelus fuscoguttatus  Serranidae  Groupers  57.0  -  1  3026  -  4%  116  -  10.0  2000  0.058  Scomberomorus commerson Scombridae Mackerel  50.0  -  2  0.007 3.010  421  -  4%  73  -  8.0  1600  0.046  Plectropomus laevis  35.0  7.1  2  0.020 3.000  901  518  4%  69  -  8.0  1600  0.043 0.041  Serranidae  Coral trout  0.014 3.033  Caranx melampygus  Carangidae Large reef assoc.  37.5  3.5  4  0.024 2.943  1054  289  8%  65  -  8.0  1600  Epinephelus coioides  Serranidae  Groupers  28.0  12.0  5  0.012 3.054  487  557  4%  31  10  11.0  2333  0.013  Plectropomus oligocanthus  Serranidae  Coral trout  38.0  -  1  0.013 3.000  724  -  4%  28  -  12.0  2400  0.012  Plectropomus maculatus  Serranidae  Coral trout  30.0  -  1  0.016 3.000  2314  -  4%  16  -  8.0  1600  0.010  Caranx sexfasciatus  Carangidae Large reef assoc.  27.0  -  1  0.028 2.836  316  -  4%  12.2  -  10.0  2000  0.006  Table A.1.2 Piscivorous fish reef health monitoring biomass calculations for SE Misool. Total number of fish sighted in Misool transects (N); standard deviation (SD); tail length (TL); species length/weight parameters (a, b). Based on unpublished data from M. Syakir (2007) TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. . Length-weight relationships from FishBase (www.fishbase.org).  54  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Species name  Family  EwE group  Caranx melampygus Sphyraena barracuda Caranx sexfasciatus Lutjanus rivulatus Gnathanodon speciosus Plectropomus leopardus Carcharhinus melanopterus Plectropomus areolatus Plectropomus maculatus Lutjanus bohar Cheilinus undulatus Caranx ignobilis Lutjanus argentimaculatus Scomberomorus commerson Plectropomus oligocanthus Gracila albimarginata Variola louti Variola albimarginata Epinephelus fuscoguttatus Gymnosarda unicolor Epinephelus coioides Epinephelus polyphekadion Plectropomus laevis Epinephelus lanceolatus Epinephelus maculatus Epinephelus tukula Epinephelus malabaricus Epinephelus caruleopunctatus  Carangidae Large reef associated Sphyraenidae Large pelagic Carangidae Large reef associated Lutjanidae Snappers Carangidae Large reef associated Serranidae Coral trout Carcharhinid Large ae sharks Serranidae Coral trout Serranidae Coral trout Lutjanidae Snappers Napoleon Labridae wrasse Carangidae Large reef associated Lutjanidae Snappers Scombridae Mackerel Serranidae Coral trout Serranidae Groupers Serranidae Groupers Serranidae Groupers Serranidae Groupers Scombridae Other tuna Serranidae Groupers Serranidae Groupers Serranidae Coral trout Serranidae Groupers Serranidae Groupers Serranidae Groupers Serranidae Groupers Serranidae Groupers  Mean Length length SD (cm) (TL; cm)  53.5 30.3 43.7 16.4 32.1 31.3 125.0 32.6 30.3 31.2 46.1 29.5 28.0 58.5 31.7 29.1 30.7 27.2 55.0 44.0 25.2 29.0 29.8 50.0 26.1 40.5 32.0 26.0  7.0 13.3 18.5 2.9 8.1 6.5 9.9 7.2 8.0 12.9 5.5 5.5 12.3 7.9 4.9 9.3 5.5 24.3 3.5 9.9 6.0 8.8 8.4 12.0 -  N  a  446 295 109 1587 172 112 1 57 54 79 22 22 41 13 27 17 27 32 3 3 11 9 4 1 10 2 1 1  0.024 0.018 0.028 0.025 0.043 0.010 0.003 0.021 0.016 0.015 0.012 0.020 0.015 0.007 0.013 0.021 0.015 0.021 0.014 0.026 0.012 0.016 0.020 0.017 0.014 0.106 0.030 0.021  b  Mean weight (g)  2.943 3077 2.945 735 2.836 1814 3.000 124 2.843 974 3.138 592 3.649 145716 3.004 982 3.000 503 3.077 701 3.115 2353 3.000 570 3.059 464 3.010 1726 3.000 498 3.004 571 3.024 606 3.004 480 3.033 3715 2.933 1716 3.054 358 3.029 470 3.000 631 3.000 2163 2.990 313 2.560 1503 2.944 811 2.907 278  Weight SD (g)  701 1863 1591 111 847 470 1251 271 617 2111 342 317 1280 369 271 480 246 2984 409 486 290 571 244 1057 -  Area Average Dives Average covered Biomass biomass Biomass spotted visibility by density per dive SD (g) (%) (m) transect (t—km-2) (g) (m2)  14 4 21 18 14 56 1 27 26 25 15 12 16 9 15 15 14 13 1 1 9 7 4 1 4 1 1 1  15194 3006 2173 2037 1408 808 856 699 298 483 498 261 209 197 148 137 146 119 61 57 43.2 39.9 27.7 23.8 23.0 16.5 8.9 3.1  39754 2913 8423 7084 1899 801 746 720 248 648 444 331 179 128 80 91 213 87 58 43 17 22 12 8 -  8.0 11.5 16.7 19.8 10.2 11.4 12.0 10.8 8.0 14.1 15.3 13.8 11.5 12.3 14.2 13.2 15.7 13.8 11.3 12.0 10.8 13.8 10.0 12.0 11.7 17.5 12.0 8.0  2385 1600 2558 2471 2388 2143 2400 2418 1717 2821 3000 2745 2333 2400 2643 2629 3038 3082 2200 2400 2075 2743 2000 2400 2833 3500 2400 1600  6.372 1.879 0.849 0.824 0.590 0.377 0.357 0.289 0.174 0.171 0.166 0.095 0.090 0.082 0.056 0.052 0.048 0.039 0.028 0.024 0.021 0.015 0.014 0.010 0.008 0.005 0.004 0.002  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  55  Table A.1.3 Reef fish family length-weight parameters. Family values represent average of Raja Ampat species. Parameters of length-weight relationships are from FishBase (Froese and Pauly 2007).  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  Scientific family name Orectolobidae Hemiscylliidae Ginglymostomatidae Carcharhinidae Dasyatididae Myliobatidae Mobulidae Moringuidae Muraenidae Ophichthidae Congridae Clupeidae Plotosidae Synodontidae Carapidae Bythitidae Batrachoididae Antennariidae Gobiesocidae Atherinidae Belonidae Hemiramphidae Holocentridae Pegasidae Aulostomidae Fistulariidae Centriscidae Syngnathidae Scorpaenidae Tetrarogidae Synanceiidae Caracanthidae Dactylopteridae Platycephalidae Centropomidae Serranidae Pseudochromidae Plesiopidae Acanthoclinidae Cirrhitidae Opistognathidae Terapontidae Priacanthidae Apogonidae  Family name wobbegongs carpetsharks nurse sharks requiem sharks rays eagle rays manta rays eels morays snake eels garden eels herrings catfish lizardfish pearlfish cuskeels toadfish frogfish clingfish silversides needlefish halfbeaks/garfish soldierfish/squirrelfish dragonfish trumpetfish cornetfish razorfish pipefish/seahorses scorpionfish waspfish stonefish/ghouls crouchers gurnards flatheads seaperch groupers/sea bass dottybacks longfins spiny basslets hawkfish jawfish/smilers grunters/tigerperch bullseyes cardinalfish  a (mean) 0.009 0.004 0.034 0.003 0.006 0.002 0.013 0.008 0.005 0.001 0.035 0.001 0.002 0.026 0.001 0.001 0.001 0.030 0.025 0.020 0.012 0.025 0.021 0.018 0.020 0.026 0.021 0.033 0.023  b (mean) 3.050 3.229 2.989 1.565 1.500 3.058 3.061 3.204 3.233 0.750 2.804 3.146 0.882 2.852 3.160 3.000 3.000 2.936 2.829 3.000 1.500 3.000 2.893 2.931 3.000 2.992 1.416 2.775 2.985  a (N) 2 1 1 7 2 2 2 2 12 3 3 23 1 6 1 1 2 4 2 5 5 17 20 1 1 1 2 16 11 1 3 1 2 5 1 54 15 2 1 8  b (N) 2 1 1 7 2 2 2 2 12 3 3 23 1 6 1 1 2 4 2 5 5 17 20 1 1 1 2 16 11 1 3 1 2 5 1 54 15 2 1 8  4 1 62  4 1 62  45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88  Scientific family name Sillaginidae Malacanthidae Echeneidae Carangidae Lutjanidae Caesionidae Gerreidae Haemulidae Lethrinidae Nemipteridae Mullidae Pempheridae Toxotidae Kyphosidae Monodactylidae Chaetodontidae Mugilidae Pomacentridae Labridae Scaridae Trichonotidae Pinguipedidae Pholidichthyidae Tripterygiidae Blenniidae Callionymidae Gobiidae Microdesmidae Ptereleotridae Xenisthmidae Ephippidae Scatophagidae Siganidae Zanclidae Acanthuridae Sphyraenidae Scombridae Bothidae Soleidae Balistidae Monacanthidae Ostraciidae Tetraodontidae Diodontidae  Family name sillagos/smelts/whitings tilefish remoras/sharksuckers trevally/jacks/scads/pompanos sea perch/snappers fusilier silverbiddy sweetlips bream/emperors whiptaisl/breams/false snappers goatfish sweepers archerfish chubs moonies butterfly fish/angelfish mullets damselfish/demoiselles/sergeants parrotfish/rainbowfish/wrasses parrotfish sanddivers sandperch convict blennies threadfin blennies blennies dragonets/scotter blennies gobies wormfish dart gobies wrigglers batfish scats spinefoots moorish idol surgeonfish/unicornfish/tangs barracudas tunas/mackerels flounders soles triggerfish filefish boxfish puffers porcupine fish  a (mean) 0.005 0.006 0.032 0.047 0.024 0.015 0.017 0.023 0.036 0.031 0.023 0.012 0.029 0.033 0.036 0.012 0.042 0.018 0.022 0.012 0.013 0.014 0.043 0.021 0.023 0.017 0.038 0.016 0.017 0.004 0.051 0.024 0.101 0.057 0.409  b (mean) 3.193 3.000 1.351 2.833 2.967 3.065 3.122 2.957 2.860 2.897 2.998 3.026 2.930 2.921 2.934 3.095 2.861 3.005 2.936 3.036 2.975 2.978 2.975 2.776 2.971 3.171 2.917 2.817 3.019 3.475 2.981 2.675 2.588 2.734 2.310  a (N) 1 5 2 22 33 12 2 10 16 12 10 3 2 3 2 57 5 109 97 29 2 7 1 5 32 6 97 2 8 1 5 1 12 1 33 5 28 2 1 14 14 3 12 2  b (N) 1 5 2 22 33 12 2 10 16 12 10 3 2 3 2 57 5 109 97 29 2 7 1 5 32 6 97 2 8 1 5 1 12 1 33 5 28 2 1 14 14 3 12 2  56  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table A.1.4 Functional group composition by fish family for LEK abundance conversion. Values are based on the number of species (per family) occurring in functional groups. EwE functional group (%)  LEK family Groupers Acanthuridae Apogonidae Aulostomidae Balistidae Caesionidae Carangidae Carcharhinidae Cirrhitidae Engraulidae Ephippidae Haemulidae Holocentridae Labridae Lethrinidae Lutjanidae Monacanthidae Mullidae Nemipteridae Ophichthidae Orectolobidae Ostraciidae Pomacentridae Scombridae Scorpaenidae Serranidae Siganidae Sum  Snappers  Skipjack tuna  Other tuna  Mackerel  Large pelagic  Medium pelagic  Small pelagic  Deepwater fish  Small demersal 28  52  35  4  Large reef associated 5 0 8 8 1 5 1  Medium reef associated  Small reef associated  11  34  Large planktivore  Anchovy  Large sharks  Small sharks  100  36  16  4 42 7  2 20  5  15 95  4 19 5  7 8 4 3 7  9 2 15 2  8  100  19 1 1  86  20 100  100  100  100  48  11 17  23  2  12  19  8 100  100  100  9 64 1  65  100 100  8 6 8 4 5 0  34 100  100  100  100  100  100  100  100  100  100  100  100  100  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  57  Table A.1.5 Fish family contributions to functional groups for RHM abundance conversion. Values are based on the number of species (per family) occurring in functional groups. EwE functional group (%)  RHM family Groupers Acanthuridae Carangidae Lutjanidae Scaridae Scombridae Serranidae Siganidae Sphyraenidae  Snappers  Skipjack tuna  Other tuna  Mackerel  Large pelagic  Medium pelagic  Small pelagic  Deepwater fish  7  14  26  4  13  Small demersal  Large reef associated  Medium reef associated  Large planktivore  5 9  26  22 18 13  87  Small planktivore  Coral trout  Eroding grazers  73  66 27  27  27  1  25  25  25 100  62  38  Scraping grazers  25  34  Sum 100 100 100 100 100 100 100 100  Chapter 1 Ecosystem Simulation Models of Raja Ampat  58  A.2. AREA CALCULATIONS  Reef area  Turtle area  Mangrove area  Depth (< 200 m)  Figure A.2.1. Habitat area. Proportion of fisher reporting high abundance in black (+), medium abundance in grey (0) and low abundance in white (-) for Raja Ampat species groups. Reef area is available from LandSat imagery (), but only to 20m depth or less. Additional reef area is determined by Indonesia Navy nautical charts to 50 m depth; this was determined using acoustic methods.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  Dugong area  59  Sea area  Figure A.2.1. cont. Dugong area is determined approximately from CRA surveys, and was entered in polygons based on expert opinon and local knowledge of occupied habitat (A. Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Unpublished data. Email: amuljadi@tnc.org.)  Chapter 1 Ecosystem Simulation Models of Raja Ampat  60  APPENDIX B - INTERVIEW DATA B.1. FISHER RESPONSES. Snappers  50 25  1980 1990 Period  50  25  2000  1980  1990  50 25 0  1970  75 50 25  1980 1990 Period  Proportion of interviews (%)  75 50 25 0  2000  1970  1980 1990 Period  2000  75 50 25  1980 1990 Period  Proportion of interviews (%)  75 50 25 0  1970  1980 1990 Period  2000  75 50 25  Batfish  100 75 50 25 0  2000  100  2000  Goatfish  100  25  0 1970  Proportion of interviews (%)  Breams  50  Emperors  100  2000  75  1970  0  1980 1990 Period  1980 1990 Period  100  2000  Sweetlips  100  1970  2000  0 1970  Proportion of interviews (%)  Fusiliers  1980 1990 Period  1990  Trevallies  100  2000  1980 Period  0  1970  25  2000  Proportion of interviews (%)  Proportion of interviews (%)  75  1980 1990 Period  50  0  1970  Sea bass  100  1970  75  Period  Surgeonfish Proportion of interviews (%)  75  0  1970  Proportion of interviews (%)  100 Proportion of interviews (%)  75  0  Proportion of interviews (%)  Rabbitfish  100  100 Proportion of interviews (%)  Proportion of interviews (%)  Groupers  100 75 50 25 0  1970  +  1980 1990 Period  0  2000  -  Figure B.1.1. Abundance scores reported by fishers by period and species. Proportion of fishers reporting high abundance in black.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  Wrasses  75 50 25 0 1980 1990 Period  50 25  1980 1990 Period  50 25 0 1980 1990 Period  100 75 50 25  1970  1980 1990 Period  50 25 0 1980 1990 Period  100 75 50 25  2000  1980 1990 Period  Proportion of interviews (%)  75 50 25 0 1980 1990 Period  2000  2000  1970  1980 1990 Period  2000  75 50 25  Boxfish  100 75 50 25 0  1970  1980 1990 Period  100  2000  Pufferfish  100  1970  25  0 1970  Proportion of interviews (%)  Trumpetfish  2000  50  Scorpionfish  0 1970  1980 1990 Period  75  1970  Proportion of interviews (%)  Proportion of interviews (%)  75  2000  100  2000  Filefish  100  1980 1990 Period  0  2000  Hawkfish  25  Damselfish  0 1970  50  1970  Proportion of interviews (%)  75  75  2000  Angelfish  100  100  0 1970  Proportion of interviews (%)  Proportion of interviews (%)  75  2000  Triggerfish  Proportion of interviews (%)  100  0 1970  Proportion of interviews (%)  Parrotfish Proportion of interviews (%)  100  Proportion of interviews (%)  Proportion of interviews (%)  Soldierfish  61  100 75 50 25 0  1970  +  1980 1990 Period  0  2000  -  Figure B.1.1. Cont. Abundance scores reported by fishers by period and species. Proportion of fishers reporting high abundance in black.  Chapter 1 Ecosystem Simulation Models of Raja Ampat  62  Butterflyfish  75 50 25 0 1980 1990 Period  50 25  1980 1990 Period  50 25 0  100 75 50 25 0  1970  1980 1990 Period  1970  1980 1990 Period  50 25 0  100 75 50 25 0  1970  1980 1990 Period  1970  100  100  25 0  1980 1990 Period  1980 1990 Period  2000  1980 1990 Period  2000  1980 1990 Period  2000  1980 1990 Period  2000  50 25  100 75 50 25  1970  Sea urchins  75 50 25 0  1970  1970  75  2000  Proportion of interviews (%)  Squids Proportion of interviews (%)  Octopus  50  2000  0  2000  75  1980 1990 Period  Anchovy Proportion of interviews (%)  Proportion of interviews (%)  75  1970  100  2000  Spanish mackerel  100  25  0  2000  Tunas  50  Eels Proportion of interviews (%)  75  75  2000  Rays  100  100  0 1970  Proportion of interviews (%)  Proportion of interviews (%)  75  2000  Wobbegongs  Proportion of interviews (%)  100  0 1970  Proportion of interviews (%)  Large sharks Proportion of interviews (%)  100  Proportion of interviews (%)  Proportion of interviews (%)  Cardinal fish  100 75 50 25 0  1970  +  1980 1990 Period  0  2000  1970  -  Figure B.1.1. Cont.Abundance scores reported by fishers by period and species. Proportion of fishers reporting high abundance in black.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  Peneaid shrimp  75 50 25 0 1980 1990 Period  50 25  1970  1980 1990 Period  50 25  75 50 25 0  1970  1980 1990 Period  1980 1990 Period  2000  100 75 50 25 0  2000  Crocodiles  25  Whales  100  0  50  1970  Proportion of interviews (%)  Proportion of interviews (%)  75  75  2000  Dolphins  100  100  0  2000  Birds Proportion of interviews (%)  75  0 1970  1970  1980 1990 Period  2000  1970  1980 1990 Period  2000  Dugongs  100  Proportion of interviews (%)  Proportion of interviews (%)  Turtles  100  Proportion of interviews (%)  100  Proportion of interviews (%)  Proportion of interviews (%)  Sea cucumbers  63  75 50 25 0  100 75 50 25 0  1970  1980 1990 Period  2000  1970  +  1980 1990 Period  0  2000  -  Figure B.1.1. Cont. Abundance scores reported by fishers by period and species. Proportion of fishers reporting high abundance in black.  Chapter 1 Ecosystem Simulation Models of Raja Ampat  64  B.2. ANALYSIS OF LEK DATA Groupers  Snappers  1.0  Rabbitfish 1.0  1.0  0.0  0.0  0.0  -1.0 1970  1980  1990  2000  Surgeonfish  -1.0  -1.0 1970  1980  1990  1970  2000  Sea bass 1.0  1.0  0.0  0.0  0.0  -1.0 1970  1980  1990  2000  Fusiliers  1980  1990  2000  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1980  1990  2000  Breams  1980  1990  2000  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1980  1990  2000  Soldierfish  1980  1990  2000  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1980  1990  2000  1990  2000  1980  1990  2000  1980  1990  2000  Parrotfish  1.0  1970  1980  -1.0 1970  Wrasses  -1.0  2000  Batfish  1.0  1970  1990  -1.0 1970  Goatfish  -1.0  1980  Emperors  1.0  1970  2000  -1.0 1970  Sweetlips  -1.0  1990  Trevallies  1.0  -1.0  1980  -1.0 1970  1980  1990  2000  1970  Figure B.2.1. Period abundance estimates from fuzzy logic algorithm rror bars show likely range around the fuzzy centroid based on the upper and lower extent of fuzzy set triangles.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  Triggerfish  65  Angelfish  Damselfish  1.0  1.0  1.0  0.0  0.0  0.0  -1.0  -1.0 1970  1980  1990  2000  Hawkfish  -1.0 1970  1980  1990  2000  Filefish  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1970  1980  1990  2000  Trumpetfish  1980  1990  2000  1.0  0.0  0.0  0.0  -1.0 1990  2000  Cardinalfish  1980  1990  2000  1.0  0.0  0.0  0.0  -1.0 1990  2000  Wobbegongs  1980  1990  2000  Eels  1.0  1.0  1.0  0.0  0.0  0.0  -1.0  1970  1980  1990  2000  2000  1970  1980  1990  2000  1970  1980  1990  2000  -1.0 1970  Rays  -1.0  1990  Large sharks  1.0  1980  1980  1970  1.0  1970  2000  -1.0 1970  Butterflyfish  -1.0  1990  Boxfish  1.0  1980  1980  1970  1.0  1970  2000  -1.0 1970  Pufferfish  -1.0  1990  Scorpionfish  1.0  -1.0  1980  -1.0 1970  1980  1990  2000  Figure B.2.1. Cont. Period abundance estimates from fuzzy logic algorithm rror bars show likely range around the fuzzy centroid based on the upper and lower extent of fuzzy set triangles.  Chapter 1 Ecosystem Simulation Models of Raja Ampat  66  Tunas  Spanish mackerel  Anchovy  1.0  1.0  1.0  0.0  0.0  0.0  -1.0  -1.0 1970  1980  1990  2000  Octopus  -1.0 1970  1980  1990  2000  Squids  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1970  1980  1990  2000  Sea cucumbers  1980  1990  2000  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1980  1990  2000  Birds  1980  1990  2000  1970  1.0  1.0  0.0  0.0  0.0  -1.0 1980  1990  2000  Crocodiles  2000  1980  1990  2000  1980  1990  2000  Whales  1.0  1970  1990  -1.0 1970  Dolphins  -1.0  1980  Turtles  1.0  1970  2000  -1.0 1970  Peneaid shrimp  -1.0  1990  Sea urchins  1.0  -1.0  1980  -1.0 1970  1980  1990  2000  1980  1990  2000  1970  Dugongs  1.0  1.0  0.0  0.0  -1.0  -1.0 1970  1980  1990  2000  1970  Figure B.2.1. Cont. Period abundance estimates from fuzzy logic algorithm rror bars show likely range around the fuzzy centroid based on the upper and lower extent of fuzzy set triangles.  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  Groupers  Snappers  2.0  1.2  0.8  0.4  1970  1980  1990  2000  -2 -1  2.0 1.5 1.0 0.5 0.0 1970  0.0  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  -2 -1  Skipjack tuna  2.5  1.6 Relative CPUE (t·km ·f )  67  1980  Other tuna  0.8 0.4  2000  0.5  1980  1990  0.8 0.4  1990  1980  -2 -1  1.2 0.8 0.4  1990 Year  0.8 0.4  2000  1.5 1.0 0.5  1980  1990 Year  2000  Medium reef associated 2.5  6.0 5.0 4.0 3.0 2.0 1.0 0.0 1970  2.0  1970  2000  -2 -1  Relative CPUE (t·km-2·f -1)  1.2  2000  0.0  1980  7.0  1.6  1990  2.5  1.6  1970  2.0  Year  0.4  Medium pelagic  Large reef associated  1990  0.8  Year  0.0  2000  Small pelagic  1980  1.2  0.0 1970  2000  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  Relative CPUE (t·km-2·f -1)  1.2  Year  -2 -1  1.0  2.0  1.6  Relative CPUE (t·km ·f )  1.5  Large pelagic  2.0  2000  1.6  Year  Small sharks  0.0 1970  2.0  0.0 1970  0.0  1990  2.0  2.5  Relative CPUE (t·km ·f )  -2 -1  1.2  1980  1980  Large sharks Relative CPUE (t·km-2·f -1)  -2 -1  Relative CPUE (t·km ·f )  Relative CPUE (t·km ·f )  1.6  0.0 1970  0.4  Year  3.0  1990 Year  0.8  0.0 1970  2000  Mackerel  2.0  1980  1.2  Year  Year  1970  1990  1.6  1980  1990 Year  2000  2.0 1.5 1.0 0.5 0.0 1970  1980  1990  2000  Year  Figure B.2.2. Biomass time series derived from LEK interviews. Relative biomass estimates from fuzzy logic analysis of LEK interview data are scaled to match relative CPUE abundance trends from 1990 to 2000. Relative biomass (t•km-2•f-1) is scaled to 1 for the first CPUE data year (f). Biomass is extrapolated back to 1970 based on a polynomial regression. Open circles: mean relative biomass from fuzzy analysis of LEK data; closed circles: Raja Ampat CPUE data from Ainsworth et al. (2007); cross-thatch: ad hoc values to correct CPUE trend; squares: omitted CPUE data. See text for additional explanation.  Chapter 1 Ecosystem Simulation Models of Raja Ampat  68  Small reef associated  Large demersal 2.5  1.5 1.0 0.5  -2 -1  2.0  2.5 Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  2.0 1.5 1.0 0.5 0.0  1980  1990  1970  2000  1980  Large planktivore  2.0 1.5 1.0 0.5  2.5 2.0 1.5 1.0 0.5 0.0 1970  0.0 2000  1980  Penaeid shrimps  2.0  4.0 c 2.0  1980  1.5 1.0 0.5  2000  0.4 0.2  1980  -2 -1  2.0  1.0  1980  1990  0.8 0.6 0.4 0.2  1980  0.6 0.4 0.2  2000  2.5  2.0 1.5 1.0 0.5 0.0 1970  2000  Epifaunal invertebrates -2 -1  -2 -1  0.8  1990 Year  Relative CPUE (t·km ·f )  Relative CPUE (t·km ·f )  1.0  2000  1.0  0.0 1970  2000  2.5  1.2  1990  1.2  Bivalves  Year  0.6  Year  Small crabs  1990  0.8  Large crabs  3.0  0.0 1970  2000  Year  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  2.0  1990  1.0  0.0 1970  2000  Lobsters  Year  Relative CPUE (t·km-2·f -1)  1990  4.0  1980  1980  Year  2.5  0.0 1970  0.5  Octopus  6.0  0.0 1970  2000  Sea cucumbers  1990  1.0  1.2  Year  1980  1.5  Year  -2 -1  -2 -1  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  4.0  2000  2.0  0.0 1970  2000  8.0  6.0  0.0 1970  1990  Shrimps and prawns  8.0  1990  2.5  Year  Year  1990  1980  3.0 -2 -1  -2 -1  2.5  1980  0.5  Deep water Relative CPUE (t·km ·f )  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  3.0  0.0 1970  1.0  Year  3.0  1990  1.5  0.0 1970  2000  Anchovy  3.5  1980  2.0  Year  Year  1970  1990  Relative CPUE (t·km ·f )  -2 -1  Relative CPUE (t·km ·f )  2.5  0.0 1970  Small demersal  1980  1990  2000  Year  Figure B.2.2. Cont. Biomass time series derived from LEK interviews.  2.0 1.5 1.0 0.5 0.0 1970  1980  1990 Year  2000  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  69  APPENDIX C - GUT CONTENT ANALYSIS C.1. STOMACH SAMPLE METHODS Table C.1.1. Fish family ratios in EwE functional groups used for diet matrix estimation. Values (%) are based on the number of species (per family) occurring in functional groups. The families shown occurred in stomach samples. EwE functional group (%)  Diet family Large reef associated  Balistidae Caesionidae Carangidae Carcharhinidae Centropomidae Chaetodontidae Dasyatidae Holocentridae Lethrinidae Lutjanidae Mullidae Nemipteridae Scaridae Scombridae Serranidae Siganidae Sphyraenidae Sum  Mackerel  Other tuna  13 1 9  Detritivore fish  Eroding grazers  Small sharks Large sharks  Rays  Medium reef Large associated planktivore  100 12 100  5 53 10  100  15 100 8 13  12 15  29 2  15 11  61 100 100  100  100  100  100  100  100  100  100  100  100  100  100  Groupers  Coral trout  Small demersal  Snappers  Butterflyfish  Scraping grazers  Large pelagic  Medium pelagic  Deepwater fish  28  30  15 100  Small Small pelagic planktivore  Balistidae Caesionidae Carangidae Carcharhinidae Centropomidae Chaetodontidae Dasyatidae Holocentridae Lethrinidae Lutjanidae Mullidae Nemipteridae Scaridae Scombridae Serranidae Siganidae Sphyraenidae Sum  Skipjack tuna  91 52  100 31 39 100  100 48 9  100  100  100  100  100  100  100  100  100  100  100  5  13  95 100  59 100  100  Chapter 1 Ecosystem Simulation Models of Raja Ampat  70  C.2. STOMACH SAMPLE RESULTS  Centropomidae  Nemipteridae  Carangidae  Caesionidae  Siganidae  Lethrinidae  Mullidae  Holocentridae  Balistidae  Serranidae  Lutjanidae  Chaetodontidae  Carcharinidae  Dasyatidae  Scaridae  Scombridae  Spyraenidae  Table C.2.1 Diet composition data from gut content analysis. Prey items have been sorted into EwE groups.  Number of fish sampled  20  20  20  20  20  20  20  20  20  20  20  11  20  20  20  20  20  Number of species sampled  1  1  8  3  8  11  6  2  2  10  11  21  2  3  5  3  2  Prey \ Predator  Juvenile groupers  0.070  Adult medium pelagic  0.053  Adult small pelagic  0.053  Adult large reef associated  0.199  0.003  0.000  0.152  0.001  0.020  0.168  0.169  0.006  0.033  Adult medium reef associated  0.199  0.003  0.000  0.152  0.001  0.020  0.168  0.169  0.006  0.033  Adult small reef associated  0.299  0.004  0.000  0.228  0.002  0.030  0.253  0.253  0.010  0.050  Adult macro algal browsing  0.100  0.001  0.000  0.076  0.001  0.010  0.084  0.084  0.003  0.017  Adult eroding grazers  0.100  0.001  0.000  0.076  0.001  0.010  0.084  0.084  0.003  0.017  Adult scraping grazers  0.100  0.001  0.000  0.076  0.001  0.010  0.084  0.084  0.003  0.017  0.038  0.600  0.483  0.019  0.300  0.242  Juvenile large reef associated  0.070  Adult anchovy  0.053  Hermatypic scleractinian corals  0.043  Non reef building scleractinian corals  0.022  0.004  0.002  0.002  0.001  Penaeid shrimps  0.155  0.228  0.484  0.018  0.031  0.180  0.138  0.053  0.013  0.387  0.178  Shrimps and prawns  0.155  0.228  0.484  0.018  0.031  0.180  0.138  0.053  0.013  0.387  0.178  Squid  0.026  0.484  0.237  Octopus  0.026  0.484  0.237  Lobsters  0.034  Large crabs  0.452  Small crabs  0.086  0.245  0.032  0.217  0.029  0.116  0.010  0.102  Herbivorous echinoids Bivalves  0.053  0.479  0.007  Epifaunal detritivorous invertebrates  0.007  Epifaunal carnivorous invertebrates  0.007  Infaunal invertebrates  0.000  0.010  0.005  0.017  0.396  Carnivorous zooplankton  0.004  Large herbivorous zooplankton  0.004  Small herbivorous zooplankton  0.004  Phytoplankton  0.005  0.000  Macro algae Sea grass  0.059  0.027 0.012  0.004  0.008  0.932  0.004  0.588  0.843 1.000  0.005  0.157  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  71  Table C.2.2. Gut content data aggregated into EwE functional groups. Grey cells indicate the interactions that are common to both the stomach sample data and the diet algorithm results.  Deepwater fish  0.049  0.168  0.032  0.018  0.125  0.039  0.049  0.168  0.032  0.018  0.125  0.253  0.253  0.253  0.010  0.010  0.050  0.084  0.156  0.058  0.073  0.253  0.048  0.027  0.187  0.007  0.025 0.062  Butterflyfish  Rays  0.025  Small sharks  0.025  0.007  Large sharks  0.007  0.003  Coral trout  Snappers  0.003  0.053  Detritivore fish  Small planktivore  0.049  0.104  0.053  0.053  Scraping grazers  Large planktivore  0.104  0.056  0.053  0.053  Eroding grazers  Small demersal  0.056  0.033  0.053  Large reef assoc.  0.033  0.006  Small pelagic  0.006  0.006  Medium pelagic  0.006  0.168  Large pelagic  0.168  0.169  Mackerel  0.169  0.168  Groupers  Ad. groupers Medium pelagic Small pelagic Large reef assoc. Medium reef assoc. Small reef assoc. Anchovy Macro algal browsing Eroding grazers Scraping grazers Hermatypic corals Soft corals Penaeid shrimps Shrimps and prawns Squid Octopus Lobsters Large crabs Small crabs Herbivorous echinoids Bivalves Epifaunal det. inverts. Epifaunal carn. inverts Infaunal inverts. Carn. zooplankton Large herb. zooplankton Small herb. zooplankton Phytoplankton Macro algae Sea grass Sum  Other tuna  0.168  Prey  Skipjack tuna  Medium reef assoc.  Predator  0.011  0.053  0.053  0.053  0.003  0.084  0.084  0.084  0.003  0.003  0.017  0.028  0.052  0.019  0.024  0.084  0.016  0.009  0.084  0.084  0.084  0.003  0.003  0.017  0.028  0.052  0.019  0.024  0.084  0.016  0.009  0.084  0.084  0.084  0.003  0.003  0.017  0.028  0.052  0.019  0.024  0.084  0.016  0.009  0.053 0.053  0.062 0.062  0.004  0.002  0.118  0.099  0.200  0.187  0.002  0.001  0.059  0.050  0.100  0.094  0.242  0.085  0.107  0.144  0.053  0.264  0.444  0.012  0.138  0.107  0.144  0.053  0.264  0.444  0.012  0.138  0.005  0.013  0.013  0.178  0.178  0.178  0.053 0.053  0.013  0.178  0.178  0.178  0.026  0.237  0.237  0.237  0.026  0.237  0.237  0.237  0.053  0.387  0.009  0.387  0.023  0.009  0.023  0.085  0.484  0.484  0.012  0.030  0.113  0.484  0.484  0.012  0.030  0.113  0.053  0.001 0.001 0.004  0.032  0.026  0.010  0.005  0.053  0.105  0.029  0.001 0.015  0.040  0.033  0.005  0.005  0.005  0.059  0.396  0.002  0.483  0.001  0.055  0.293  0.001  0.001  0.001  0.001  0.021  0.003  0.005  0.001  0.000  0.001  0.588  0.800  0.498  0.149  0.094  0.004  0.143  0.000  1.000  1.000  1.000  1.000  1.000  1.000  0.003 0.003 0.003 0.006  0.009  0.007  0.004 0.004 0.004 0.000  1.000  1.000  0.005  0.005  0.005  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.002 1.000  0.004  0.007  0.001  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000  72  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table C.2.3. Diet algorithm results. The grey cells indicate interactions common to both the stomach sample data and the diet algorithm results. The diet algorithm is described in Ainsworth et al. (2007); it uses co-habitation, predator gape and prey body size as inputs.  0.001  0.000  0.001 0.001 0.001 0.000 0.005 0.005 0.005 0.005  0.008  0.007  0.003  0.001  0.009  0.002  0.000  0.000  0.001  0.002  0.051  0.074  0.016  0.010  0.088  0.026  0.006  0.000  0.000  0.008  0.003  0.053  0.056  0.018  0.007  0.067  0.017  0.012  0.000  0.000  0.006  0.001  0.027  0.032  0.008  0.004  0.040  0.011  0.000  0.000  0.000  0.003  0.041  0.002  0.002  0.012 0.012  0.000  0.040  0.012  0.012 0.012 0.000  0.013 0.012  0.000  0.000  0.000  0.000  0.012  0.000  0.000  0.000  0.000  0.012  0.000  0.000  0.000  0.000  0.002  0.024  0.022  0.001  0.012  0.012  0.000  0.001  0.012  0.047  0.068  0.033  0.002  0.003  0.001  0.012  0.001  0.000  0.001  0.005  0.080  0.075  0.029  0.008  0.089  0.021  0.019  0.000  0.000  0.007  0.004  0.040  0.033  0.015  0.003  0.038  0.008  0.024  0.000  0.000  0.003  0.000  0.013  0.017  0.133  0.107  0.051  0.009  0.120  0.025  0.107  0.000  0.000  0.009  0.000  0.026  0.012  0.165  0.116  0.237  0.321  0.168  0.011  0.116  0.088  0.002  0.041  0.012  0.066  0.088  0.070  0.105  0.047  0.004  0.036  0.036  0.006  0.024  0.012  0.032  0.048  0.033  0.050  0.022  0.002  0.017  0.018  0.005  0.013  0.012  0.003  0.003  0.003  0.004  0.002  0.000  0.002  0.002  0.000  0.001  0.012  0.003  0.004  0.003  0.005  0.002  0.000  0.002  0.002  0.000  0.001  0.032  0.050  0.190  0.159  0.113  0.058  0.043  0.183  0.002  0.060  0.106  0.018  0.001  0.024  0.000  0.020  0.020  0.042  0.107  0.086  0.062  0.031  0.021  0.096  0.001  0.031  0.089  0.011  0.002  0.015  0.000  0.025  0.003  0.006  0.015  0.012  0.009  0.004  0.003  0.013  0.000  0.004  0.013  0.002  0.001  0.002  0.000  0.014  0.032  0.052  0.180  0.148  0.106  0.054  0.039  0.168  0.002  0.055  0.117  0.017  0.001  0.022  0.000  0.019  0.003  0.002  0.003  0.005  0.002  0.000  0.002  0.002  0.000  0.001  0.012  0.001  0.001  0.001  0.002  0.001  0.000  0.001  0.001  0.000  0.000  0.012  0.054  0.045  0.081  0.107  0.058  0.004  0.039  0.030  0.002  0.015  0.012  0.005  0.005  0.005  0.008  0.004  0.000  0.003  0.003  0.000  0.001  0.012  0.187  0.000  0.010  0.043  0.187  0.000  0.010  0.043  0.187  0.000  0.010  0.043  0.060  0.000  0.006  0.000  0.003  0.003  0.003  0.072  0.045  0.126  0.010  0.073  0.006  0.001  0.018  0.007  0.022  0.001  0.055  0.005  0.007  0.003  0.000  0.000  0.004  0.002  0.000  0.000  0.000  0.000  0.045  0.038  0.009  0.009  0.002  0.018  0.006  0.009  0.136  0.003  0.103  0.001  0.037  0.020  0.079  0.006  0.020  0.313  0.019  0.001  0.020  0.027  0.001  0.001  0.008  0.006  0.017  0.000  0.002  0.011  0.000  0.000  0.000  0.008  0.000  0.012  0.005 0.000  0.001  0.003 0.001  0.027  0.011  0.003  0.007  0.004  0.005  0.009  0.031  0.000  0.001  0.003  0.001  0.015  0.008  0.006  0.003  0.042  0.003  0.149  0.073  0.003  0.014  0.004  0.005  0.009  0.068  0.001  0.001  0.240  0.001  0.107  0.053  0.008  0.020  0.042  0.003  0.149  0.073  0.003  0.014  0.004  0.005  0.009  0.068  0.001  0.001  0.240  0.001  0.107  0.053  0.008  0.020  0.042  0.004  0.002  0.000  0.000  0.000 0.020  0.018  0.000 0.011  0.000  0.000  0.011  0.004  0.000  0.010  0.000 0.009  0.098  0.000  0.007  0.003  0.003  0.001  0.002  0.005  0.004  0.000  0.000  0.020  0.042  0.005 0.001  0.011  0.008  0.005  0.233  0.003  0.013  0.006  0.000  0.010  0.012  0.009  0.009  0.021  0.018  0.011  0.011  0.004  0.010  0.009  0.099  0.012  0.004  0.003  0.005  0.034  0.031  0.012  0.021  0.044  0.005  0.021  0.020  0.011  0.011  0.004  0.010  0.009  0.099  0.073  0.005  0.003  0.005  0.053  0.124  0.035  0.063  0.055  0.005  0.021  0.020  0.011  0.011  0.004  0.010  0.009  0.099  0.072  0.005  0.003  0.005  0.048  0.073  0.033  0.063  0.055  0.005  0.001  0.002  0.013  0.020  0.001  0.057  0.003 0.006  0.000  0.002 0.028  0.000  0.000  0.000  0.016  0.006  0.019  0.007  0.004  0.006  0.008  0.004  0.006  0.008  0.000  0.020 0.009  0.001  0.058  0.005  0.026 0.029  0.003 0.001  0.044  0.004 0.038  0.038  0.012  0.484  0.008  0.082  0.174  0.269  0.046  0.000  0.003  0.006  0.005  0.005  0.014  0.065  0.008  0.033  0.000  0.038  0.011  0.484  0.005  0.078  0.160  0.269  0.033  0.011  0.018  0.027 0.010  0.000  0.058  0.072  0.027  0.100  0.000  0.007  0.010  0.000  0.058  0.072  0.027  0.100  0.001  0.001  0.004  0.054  0.006  0.095  0.001  0.001  0.095  1.000  0.003 1.000  1.000  1.000  0.004  0.054  0.006  0.000  0.001  0.002  0.004  0.011  0.030  0.001  0.038  1.000  1.000  1.000  1.000  1.000  0.268  0.001 1.000  0.006  0.007  0.005  0.008 1.000  0.003 0.001  0.000  0.001  1.000  0.006 0.002  1.000  1.000  0.000  0.002  0.003  1.000  1.000  0.299 0.299  0.000 1.000  0.002 1.000  1.000  Detritivore fish  0.000  Scraping grazers  0.004  Eroding grazers  0.003  Deepwater fish  0.001  Small planktivore  0.007  Large planktivore  0.000  Small demersal  Medium reef assoc.  0.015  Small pelagic  0.018  Medium pelagic  0.002  Large pelagic  0.008  0.025  Butterflyfish  0.001  0.050  Rays  0.016  0.036  Small sharks  0.020  0.021  Large sharks  0.002  0.027  Coral trout  0.029  Mackerel  0.054  Other tuna  0.040  Skipjack tuna  0.026  Snappers  0.029  Groupers  Prey Mysticetae Pisc. odontocetae Deep. odontocetae Birds Reef assoc. turtles Green turtles Oceanic turtles Crocodiles Ad. groupers Ad. snappers Ad. Napoleon wrasse Skipjack tuna Other tuna Mackerel Billfish Ad. large sharks Ad. small sharks Adult rays Ad. butterflyfish Cleaner wrasse Large pelagic Medium pelagic Small pelagic Large reef assoc. Medium reef assoc. Small reef assoc. Large demersal Small demersal Large planktivore Small planktivore Anchovy Deepwater fish Macro algal browsing Eroding grazers Scraping grazers Detritivore fish Azooxanthellate corals Hermatypic corals Non reef building corals Soft corals Anemonies Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers Lobsters Large crabs Small crabs Crown of thorns Giant triton Herbivorous echinoids Bivalves Sessile filter feeders Epifaunal det. inverts. Epifaunal carn. inverts Infaunal inverts. Jellyfish and hydroids Carn. zooplankton Large herb. zooplankton Small herb. zooplankton Phytoplankton Macro algae Sea grass Fishery discards Detritus Import Juvenile fish Omit (unidentified) Sum  Large reef assoc.  Predator  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  73  Table C.2.4. Stomach samples versus Raja Ampat model. Values shown are weighting factors necessary to increase or decrease Raja Ampat model diet composition to match stomach samples [i.e., DCsamples/DCmodel]. Values > 1 are minor interactions in the model that appear more important according to stomach samples; values < 1 are major interactions in the model that appear less important according to the stomach samples. The values shown represent the top 25 percentile of interactions that were identified by the stomach sampling, but missed by the diet algorithm of Ainsworth et al. (2007). Grey cells indicate diet parameters improved in the final models (this volume). Prey \ Predator  Snappers  Skipjac k tuna  Other tuna  Large sharks  Butterfly fish  Large pelagic  Large reef assoc.  Large reef assoc.  Medium reef assoc.  Large plank .  Deep. fish  1.21  48.73  31.75  6.24  Medium reef  3.75  24.96  assoc. Small reef  12.06  9.71  7.31  15.87  812  105  840  317  26.74  assoc. Macro algal  153  browsing Eroding  444  > 1000  grazers Scraping  3.48  > 1000 20.80  grazers Hermatypic  > 1000  382  corals Soft corals Shrimps and  1372 0.50  0.18  prawns Squid  0.51  Octopus  0.04 118  344  120  Large crabs  52.47  Small crabs  13.14  1.29  Bivalves  2.39  7.33  Epifaunal det.  0.46  0.21  0.09  0.02  0.01  0.02  0.10  0.02  0.05  0.01  inverts. Epifaunal carn. inverts Infaunal  0.38  0.15  inverts. Large herb.  0.38  zooplankton Macro algae Sea grass  3.92  > 1000 > 1000  0.00  0.13  0.04  74  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table C.2.5. Stomach samples versus diet algorithm. Weighting factors required to increase or decrease Raja Ampat model diet composition (DC) to match algorithm estimate [DCsamples/DCalgorithm]. Interactions presented in this table represent the most important (top 25 percentile) predator-prey interactions which were identified by the stomach sampling but show discrepancies with the preliminary Raja Ampat model of Ainsworth et al. (2007) or the unprocessed diet algorithm results. Grey cells indicate diet parameters improved in the final models (this volume).  Prey \ Predator  Snappers  Skipjack tuna  Other tuna  Large sharks  Butterfly fish  Large pelagic  Large reef assoc.  Large reef assoc.  Mediu m reef assoc.  Large plank.  Deep. fish  0.56  20.25  0.77  10.48  Medium reef assoc.  8.40  Small reef assoc.  5.22  Macro algal browsing  36.12  Eroding grazers  3.20  121  23.28  Scraping grazers  10.48  13.43  3.55  186  24.31  199  73.47  13.85  Hermatypic corals  0.01  > 1000  Soft corals  15.72  5.24 5.24  10.06  > 1000  Shrimps and prawns  0.35  Squid  0.71  Octopus  0.17  0.03 30.36  37.63  27.75  Large crabs  0.98  Small crabs  0.25  0.29  Bivalves  2.74  22.83  Epifaunal det. inverts.  0.03  0.03  0.06  Epifaunal carn. inverts  0.02  0.01  0.05  0.43  0.04  0.08  0.01  Infaunal inverts.  0.51  Large herb. zooplankton  1.37  Macro algae  61.70  Sea grass  > 1000 > 1000  0.01  0.17  0.17  0.12  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  75  APPENDIX D - EWE PARAMETERIZATION D.1. BIOMASS Table D.1.1 Biomass for sub-area models (t•km-2). Biomasses for Kofiau, Dampier St. and SE Misool models are based on ratios and assumptions listed in Table D.1.2; the method used is indicated by Ref #. Modifier indicates subsequent changes made during balancing and tuning. A modifier greater than one indicates that biomass was increased during tuning; less than one indicates it was decreased (see Sections 2.3 and 2.4). Group #  Functional group  1  Mysticetae  2  Pisc. odontocetae  3  Deep. odontocetae  4  Dugongs  5  Birds  6  Reef assoc. turtles  7  Green turtles  8  Oceanic turtles  9  Crocodiles  10  Ad. groupers  11  Sub. groupers  12  Juv. groupers  13  Ad. snappers  14  Sub. snappers  15  Juv. snappers  16  Ad. Napoleon wrasse  17  Sub. Napoleon wrasse  18  Juv. Napoleon wrasse  19  Skipjack tuna  20  Other tuna  21  Mackerel  22  Billfish  23  Ad. coral trout  24  Juv. coral trout  25  Ad. large sharks  26  Juv. large sharks  27  Ad. small sharks  28  Juv. small sharks  29  Whale shark  30  Manta ray  31  Adult rays  32  Juv. rays  33  Ad. butterflyfish  34  Juv. butterflyfish  35  Cleaner wrasse  36  Ad. large pelagic  37  Juv. large pelagic  38  Ad. medium pelagic  39  Juv. medium pelagic  40  Ad. small pelagic  41  Juv. small pelagic  42  Ad. large reef assoc.  43  Juv. large reef assoc.  44  Ad. medium reef assoc.  45  Juv. medium reef assoc.  46  Ad. small reef assoc.  Kofiau  Modifier  0.066  9  0.060  11  0.181  9  0.007  6  0.366  12  0.036  4  0.086  7  0.091 0.002  7 1.2  1.050  1  0.090  3  2.531  1  1.305  1  0.941  3  0.015  1.6  0.026  1.6  0.005  1.4  0.803  4 4  2 2.2  1.644  1 9  0.491  1  0.107  3  0.122  9  0.106  3  0.041  1.2  0.017  1.2  0.006  4 4 9  0.9  0.205  11 11  0.078  11  0.205  4  0.068 0.008  4  11  0.020  0.003  8 1  0.327  0.100  Ref #  4 1.1  0.063  4 11  0.038  3  0.042  2  0.064  3  0.022  2  0.034  3  3.617  10.0  2.290  10.0  2.853  142.0  2.356  142.0  0.874  4.0  1 3 2 3 4  Dampier St.  Modifier  0.048  9  0.031  11  0.133  9  0.131  6  0.366  12  0.049  4  0.072  7  0.076 0.001  7 0.9  3.118 2.2  0.268  2.4  1.176  0.013  4 0.9  0.411  11 2.0  13 2.6  0.089  3  0.047  4  0.019  4  0.004  0.9  0.002  1.1  0.105  1.1  11  0.007  1.1  0.011  1.1  2.692 2.147  13  11  0.019  3.260  11  13  0.032  5.157  11  13  0.134  8.146  9  11 3.7  3.564  0.064  13 9  0.077  0.042  11 9  0.511  1.188  4 11  1.205  0.151  13  4  0.321  0.173  13  13  0.023  0.100  13  13 1.1  0.437  0.004  8 13  0.971  0.606  Ref #  11 11 11 11 4 4 4 4 13  SE Misool  Modifier  0.023  9  0.033  11  0.064  9  0.226  6  0.366  12  0.05  4  0.138  7  0.146 0.002  7 1.2  0.173  1  0.015  3  0.716  1  0.369  1  0.266  3  0.060  1  0.106  1  0.019  4.2  0.396  0.9  0.096  2.1  0.100  1.2  0.576  4 11 2 1 9  0.910  1  0.198  3  0.356  1  0.308  3  0.047  4  0.020  4  0.002  9  0.002  11  0.114  11  0.044  11  0.281  4 3.4  0.010  4 4  0.125  0.1  0.075  0.1  0.084  0.1  0.128  0.1  0.381  1 11 2 3 2  0.581  3  7.406  0.9  9.724  1.9  0.943  2.1  0.779  2.1  0.300  8 1  0.054  0.323  Ref #  1 3 2 3 4  76  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table D.1.1. Cont. Biomass for sub-area models (t•km-2). Group #  Functional group  47  Juv. small reef assoc.  48  Ad. large demersal  Kofiau  Modifer  0.455  4.0  0.147  Ref # 4 11  Dampier St. 1.119  Modifier 0.3  0.075  Ref # 13 11  SE Misool 0.156  Modifier  Ref # 4  0.082  11  49  Juv. large demersal  0.157  11  0.080  11  0.088  50  Ad. small demersal  0.035  2  0.114  11  0.116  5.1  11 2  51  Juv. small demersal  0.025  3  0.080  11  0.082  5.1  3  52  Ad. large planktivore  2.202  1  4.405  0.1  13  1.264  53  Juv. large planktivore  1.954  3  3.909  0.1  13  1.122  54  Ad. small planktivore  0.035  2  0.345  0.1  13  0.414  12.5  55  Juv. small planktivore  0.052  3  0.512  0.0  13  0.615  12.5  56  Ad. anchovy  0.858  2.0  10  1.003  10  1.825  10  2 3 2 3  57  Juv. anchovy  1.279  2.0  10  1.496  10  2.721  10  58  Ad. deepwater fish  0.618  30.8  2  0.876  9  0.376  2  59  Juv. deepwater fish  0.817  30.8  3  1.159  9  0.497  3  60  Ad. macro algal browsing  0.071  10  0.167  10  0.304  10  61  Juv. macro algal browsing  0.142  10  0.334  10  0.608  10  62  Ad. eroding grazers  0.588  2  7.533  13  1.015  2  63  Juv. eroding grazers  0.286  3  3.667  13  0.494  64  Ad. scraping grazers  4.000  0.4  2  4.980  13  2.000  0.1  65  Juv. scraping grazers  4.160  0.1  3  23.716  13  9.525  0.1  3  66  Detritivore fish  0.019  11  0.223  13  0.016  1.6  11  3 2  67  Azooxanthellate corals  0.506  4  2.058  3.0  4  1.388  2.0  4  68  Hermatypic corals  0.738  4  3.000  3.0  4  2.024  2.0  4  69  Non reef building corals  0.506  4  2.058  3.0  4  1.388  2.0  4  70  Soft corals  0.506  4  2.058  3.0  4  1.388  2.0  4  71  Calcareous algae  0.029  10  0.201  3.0  10  0.244  2.0  10  72  Anemonies  1.416  10  1.002  3.0  10  1.216  2.0  10  73  Penaeid shrimps  2.317  11  3.561  3.0  11  2.576  2.0  11  74  Shrimps and prawns  2.317  11  3.561  3.0  11  2.576  2.0  11  75  Squid  0.828  11  0.423  3.0  11  0.306  2.0  11  76  Octopus  1.159  11  1.782  3.0  11  1.288  2.0  11  77  Sea cucumbers  1.718  11  1.728  3.0  11  1.250  2.0  11  78  Lobsters  0.254  11  0.650  5.0  11  1.128  8.0  11  79  Large crabs  0.962  2.9  11  0.510  3.0  11  0.368  2.0  11  80  Small crabs  1.222  3.7  11  0.510  3.0  11  0.368  2.0  11  81  Crown of thorns  0.185  4  0.750  3.0  4  0.506  2.0  4  82  Giant triton  0.058  11  0.090  3.0  11  0.064  2.0  11  83  Herbivorous echinoids  1.478  11  1.287  3.0  11  0.930  2.0  11  84  Bivalves  10.65  11  16.365  3.0  11  11.836  2.0  11  85  Sessile filter feeders  5.307  11  8.157  3.0  11  5.900  2.0  11  86  Epifaunal det. inverts.  3.469  2.1  11  2.493  3.0  11  1.804  2.0  11  87  Epifaunal carn. inverts  8.201  1.3  11  9.975  3.0  11  7.214  2.0  11  88  Infaunal inverts.  31.774  11  48.837  3.0  11  35.322  2.0  89  Jellyfish and hydroids  0.100  12  0.300  3.0  12  0.100  12  90  Carn. zooplankton  1.000  12  3.000  3.0  12  1.000  12  91  Large herb. zooplankton  0.560  12  1.680  3.0  12  0.560  12  92  Small herb. zooplankton  2.43.0  12  7.290  3.0  12  2.430  12  93  Phytoplankton  26.100  12  26.100  12  26.100  12  94  Macro algae  11.264  10  52.686  2.0  10  47.919  10  2.0  8  9.9  3.0  1.5  1.8  11  95  Sea grass  35.006  8  46.940  8  32.644  96  Mangroves  25.009  5  14.399  5  15.536  5  97  Fishery discards  6.817  14  20.265  14  15.600  14  98  Detritus  115.87  11  59.366  11  64.405  5.1  11  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  77  Table D.1.2. Biomass estimation method and data references. All biomass estimation methods rely on a relative physical ratio versus the Raja Ampat 2005 model (Table D.1.3.) except 1, 2, 3, 13 and 14. Ref # 1  Estimation method Reef health monitoring dive transect data (by species) Kofiau: A. Muljadi (unpublished data). Misool: M. Syakir (unpublished data). TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413.  2  Rationale  BHS EBM project sampling  Reef health monitoring dive transect data (by family). Biomass per family is assigned to EwE groups in the same proportion as the relative species count occurring in groups. Citation as Ref # 1.  BHS EBM project sampling  Immature life history stanza biomasses from EwE multistanza model (Christensen and Walters, 2004). Maturation and growth parameters provided in Ainsworth et al. (2007).  Age-structure of model  Coral reef area coverage. Reef area is available from LandSat imagry to 20m depth; (2000-2002) (NASA Landsat Program, 2006). Reef area to 50m depth is determined by Indonesia Navy nautical charts (TNI AL, 2002) obtained with acoustical methods; Additional ratios from literature sources (see Table 2.1: Hard coral coverage reported for Raja Ampat.).  Habitat area  Mangrove area. Based on LandSat imagry (2000-2002) (NASA Landsat Program, 2006), assembled by M. Barmawi (unpublished data); contact: Joanne Wilson, TNC CTC. Jl Pengembak 2, Sanur, Bali, Indonesia joanne_wilson@tnc.org.  Habitat area  Dugong area. BHS EBM Project Coastal Rural Appraisal Surveys. TNC. (A. Muljadi. TNC-CTC. Jl Gunung Merapi No. 38, Kampung Baru, Sorong, Papua, Indonesia 98413. Email: amuljadi@tnc.org. unpublished data).  Habitat area  7  Turtle nesting habitat area. BHS EBM Project Coastal Rural Appraisal Surveys. Citation as Ref # 6.  Habitat area  8  Coast line. Indonesian Navy nautical charts (TNI AL, 2002) digitized by M. Barmawi (unpublished data); contact: Joanne Wilson, TNC CTC. Jl Pengembak 2, Sanur, Bali, Indonesia joanne_wilson@tnc.org.  Physical oceanography  9  Deep area (>200 m) to total water area ratio. Indonesian Navy nautical charts. Citation as Ref # 8.  Physical oceanography  10  Shallow area (<200 m) to total water area ratio. Indonesian Navy nautical charts. Citation as Ref # 8.  Physical oceanography  11  Water to land area ratio. Indonesian Navy nautical charts. Citation as Ref # 8.  Physical oceanography  12  Biomass in sub-area models assumed same as Raja Ampat average. Values from Ainsworth et al. (2007).  Adapted from other source  13  Waigeo transect abundance COREMAP (2001).  Adapted from other source  14  Based on human population (inter-island ratios used for sub-area models). BHS EBM Project CI Valuation Report: Dohar and Anggraeni (2007).  Human population  3  4  5  6  78  Chapter 1 Ecosystem Simulation Models of Raja Ampat  D.2. CATCH Table D.2.1 EwE catch matrix for 2005 Raja Ampat model (kg•km-2)  Sum  Shrimp trawl  Foreign fleet  Lift net  Hook and line  Pole and line  Purse seine  Trolling  Blast fishing  Diving cyanide  Diving live fish  Diving spear  Portable trap  Permanent trap  Driftnet  Shore gillnet  Reef gleaning  Group  Spear and harpoon  Gear type  Ad. groupers  1.45  2.89  1.45  7.23  7.23  1.45  72.30  94.0  Sub. groupers  0.73  1.47  0.73  3.67  3.67  0.73  36.70  47.7  Juv. groupers  0.49  0.98  0.49  19.60  21.6  Ad. snappers  4.36  8.71  8.71  4.36  4.36  84.00  114.5  Sub. snappers  4.36  8.71  8.71  4.36  4.36  Juv. snappers  0.97  1.94  1.94  0.97  55.50  86.0  0.97  25.40  32.2  Ad. Napoleon wrasse  0.80  0.80  0.16  28.90  30.7  Sub. Napoleon wrasse  0.80  0.80  0.16  14.70  16.5  0.21  5.18  Juv. Napoleon wrasse Skipjack tuna Other tuna  45.80  229.00  75.00  80.50  608.3  11.50  5.95  13.70  12.90  7.41  51.5  37.40  9.64  Mackerel Billfish  5.4  178.00  48.20  95.2  87.40  Ad. coral trout  1.18  1.18  1.18  1.18  1.18  0.31  Juv. coral trout  0.12  0.12  0.12  0.12  0.12  0.03  87.4 6.2 0.6  Ad. large sharks  44.70  44.7  Juv. large sharks  4.96  5.0  Ad. small sharks  9.94  Juv. small sharks  9.9  1.10  Adult rays  6.07  Juv. rays Ad. butterflyfish  11.20  Juv. butterflyfish  1.12  6.07  6.07  0.61  0.61  0.61  0.61  11.20  11.20  11.20  11.20  1.12  2.4 1.00  2.00  59.0  1.12  0.29  0.73  0.73  0.73  0.73  0.19  13.60  10.90  8.15  8.15  13.60  Juv. large pelagic  1.81  1.45  1.09  1.09  1.81  7.3  Ad. medium pelagic  3.02  2.42  1.81  1.81  3.02  12.1  Juv. medium pelagic  1.12  24.3  Ad. large pelagic  Cleaner wrasse  1.12  1.1  6.07  5.9 3.1 54.4  1.34  1.07  0.81  0.81  1.34  5.4  Ad. small pelagic  12.50  12.50  9.39  9.39  3.13  15.70  62.6  Juv. small pelagic  1.39  1.39  1.04  1.04  0.35  1.74  41.40  41.40  41.40  37.80  10.90  148.00  Ad. large reef assoc. Juv. large reef assoc.  41.40  6.9 362.3  8.06  8.06  8.06  8.06  7.01  2.12  34.30  75.7  Ad. medium reef assoc.  22.80  22.80  22.80  22.80  21.10  6.01  84.60  202.9  Juv. medium reef assoc.  2.28  2.28  2.28  2.28  1.71  0.60  25.90  37.3  Ad. small reef assoc.  13.50  13.50  13.50  13.50  10.10  3.54  42.80  110.4  Juv. small reef assoc.  1.35  1.35  1.35  1.35  1.01  0.35  9.47  16.2  Ad. large demersal  9.71  9.71  7.28  2.56  9.34  Juv. large demersal  1.94  1.94  1.46  0.51  3.34  Ad. small demersal  17.40  17.40  17.40  4.59  1.94  1.94  0.51  6.3  4.83  4.83  1.27  25.4  Juv. small demersal  1.94  Ad. large planktivore  4.83  4.83  4.83  38.6 9.2 56.8  Juv. large planktivore  4.78  4.78  4.78  4.78  4.78  1.26  25.2  Ad. small planktivore  4.52  4.52  4.52  4.52  4.52  1.19  23.8  Juv. small planktivore  3.41  3.41  3.41  3.41  3.41  0.90  Ad. anchovy  79.90  63.90  47.90  47.90  116.00  355.6  Juv. anchovy  8.88  7.10  5.33  5.33  8.88  35.5  Ad. deepwater fish  4.13  4.13  4.13  4.13  Juv. deepwater fish  0.46  0.46  0.46  0.46  Ad. macro algal browsing  0.75  0.75  0.75  0.75  Juv. macro algal browsing  0.07  0.07  0.07  17.9  16.5 1.8 0.10  0.07  3.1  0.02  0.3  Ad. eroding grazers  0.25  0.25  0.25  0.25  0.03  1.0  Juv. eroding grazers  1.3E-02  1.3E-02  1.3E-02  1.3E-02  3.4E-03  0.1  Ad. scraping grazers  20.50  20.50  20.50  20.50  2.70  84.7  Juv. scraping grazers  1.99  1.99  1.99  1.99  0.52  8.5  Detritivore fish  1.75  1.75  1.75  1.75  0.23  7.2  2.00  2.0  Hermatypic corals Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers  695.00  695.0  81.50  81.5  30.20  30.2  4.7E-03  1.5E-02  4.9E-03  2.5E-04  0.0  2.12  6.68  2.23  0.11  11.1  Lobsters  262.00  87.20  4.36  353.6  Large crabs  9.81  3.27  0.16  13.2  Small crabs  9.81  3.27  0.16  13.2  Giant triton  4.40  1.47  0.07  5.9  Herbivorous echinoids  9.81  3.27  0.16  13.2  Bivalves  28.30  Sessile filter feeders  28.3  4.80  4.8  Epifaunal det. inverts.  10.90  3.64  0.18  14.7  Epifaunal carn. inverts  12.80  4.27  0.21  17.3  Sum  166.0  359.0  300.0  259.0  289.0  251.0  120.0  12.0  13.0  67.0  277.0  52.0  280.0  858.0  240.0  88.0  777.0  4408.0  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  79  Table D.2.2 EwE catch matrix for 2005 Dampier St. model (kg•km-2)  Sum  Shrimp trawl  Foreign fleet  Lift net  Hook and line  Pole and line  Purse seine  Trolling  Blast fishing  Diving cyanide  Diving live fish  Diving spear  Portable trap  Permanent trap  Driftnet  Shore gillnet  Reef gleaning  Group  Spear and harpoon  Gear type  Ad. groupers  1.45  2.89  1.45  7.23  7.23  1.45  72.30  Sub. groupers  0.73  1.47  0.73  3.67  3.67  0.73  36.70  Juv. groupers  0.49  0.49  19.60  21.6  Ad. snappers  4.36  8.71  8.71  4.36  4.36  84.00  114.5  Sub. snappers  4.36  8.71  8.71  4.36  4.36  55.50  86.0  Juv. snappers  0.97  1.94  1.94  0.97  0.98  94.0 47.7  0.97  25.40  32.2  Ad. Napoleon wrasse  0.80  0.80  0.16  28.90  30.7  Sub. Napoleon wrasse  0.80  0.80  0.16  Juv. Napoleon wrasse  14.70  0.21  Skipjack tuna Other tuna  45.80  11.50  5.95  Mackerel Billfish  16.5  5.18 178.00  229.00  75.00  13.70  12.90  37.40  9.64  5.4 80.50  608.3  7.41  51.5  48.20  95.2  87.40  87.4  Ad. coral trout  1.18  1.18  1.18  1.18  1.18  0.31  6.2  Juv. coral trout  0.12  0.12  0.12  0.12  0.12  0.03  0.6  Ad. large sharks  44.70  Juv. large sharks  4.96  44.7 5.0  Ad. small sharks  9.94  9.9  Juv. small sharks  1.10  1.1  Adult rays  6.07  6.07  6.07  6.07  Juv. rays  0.61  0.61  0.61  0.61  11.20  11.20  11.20  11.20  Ad. butterflyfish  11.20  Juv. butterflyfish  1.12  1.12  Cleaner wrasse Ad. large pelagic Juv. large pelagic  2.00  1.12  0.29  0.73  0.73  0.73  0.73  0.19  10.90  8.15  8.15  5.9 3.1 13.60 1.81  7.3  3.02  2.42  1.81  1.81  3.02  12.1  1.34  1.07  0.81  0.81  1.34  5.4  Ad. small pelagic  12.50  12.50  9.39  9.39  3.13  15.70  62.6  Juv. small pelagic  1.39  1.39  1.04  1.04  0.35  1.74  41.40  41.40  41.40  37.80  10.90  148.00  2.12  Juv. large reef assoc.  1.09  54.4  Ad. medium pelagic  41.40  1.09  59.0  Juv. medium pelagic  Ad. large reef assoc.  1.45  1.12  2.4 1.00  13.60 1.81  1.12  24.3  6.9 362.3  8.06  8.06  8.06  8.06  7.01  34.30  75.7  Ad. medium reef assoc.  22.80  22.80  22.80  22.80  21.10  6.01  84.60  202.9  Juv. medium reef assoc.  2.28  2.28  2.28  2.28  1.71  0.60  25.90  37.3  Ad. small reef assoc.  13.50  13.50  13.50  13.50  10.10  3.54  42.80  110.4  Juv. small reef assoc.  1.35  1.35  1.35  1.35  1.01  0.35  9.47  16.2  Ad. large demersal  9.71  9.71  7.28  2.56  9.34  38.6  Juv. large demersal  1.94  1.94  1.46  0.51  3.34  Ad. small demersal  17.40  17.40  17.40  4.59  9.2 56.8  Juv. small demersal  1.94  1.94  1.94  0.51  6.3  Ad. large planktivore  4.83  4.83  4.83  4.83  4.83  1.27  25.4  Juv. large planktivore  4.78  4.78  4.78  4.78  4.78  1.26  25.2  Ad. small planktivore  4.52  4.52  4.52  4.52  4.52  1.19  23.8  Juv. small planktivore  3.41  3.41  3.41  3.41  3.41  0.90  79.90  63.90  47.90  47.90  116.00 8.88  Ad. anchovy Juv. anchovy  8.88  7.10  5.33  5.33  Ad. deepwater fish  4.13  4.13  4.13  4.13  Juv. deepwater fish  0.46  0.46  0.46  0.46  Ad. macro algal browsing  0.75  0.75  0.75  0.75  Juv. macro algal browsing  0.07  0.07  0.07  17.9 355.6 35.5 16.5 1.8  0.10  0.07  3.1  0.02  0.3  Ad. eroding grazers  0.25  0.25  0.25  0.25  0.03  1.0  Juv. eroding grazers  1.3E-02  1.3E-02  1.3E-02  1.3E-02  3.4E-03  0.1  Ad. scraping grazers  20.50  20.50  20.50  20.50  2.70  84.7  Juv. scraping grazers  1.99  1.99  1.99  1.99  0.52  8.5  Detritivore fish  1.75  1.75  1.75  1.75  0.23  7.2  Hermatypic corals  2.00  2.0  Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers  695.00  695.0  81.50  81.5  30.20 4.7E-03 2.12  1.5E-02  4.9E-03  2.5E-04  30.2 0.0  6.68  2.23  0.11  11.1  262.00  87.20  4.36  353.6  Large crabs  9.81  3.27  0.16  13.2  Small crabs  9.81  3.27  0.16  13.2  Giant triton  4.40  1.47  0.07  5.9  9.81  3.27  0.16  13.2  Lobsters  Herbivorous echinoids Bivalves  28.30  Sessile filter feeders  4.80  Epifaunal det. inverts.  4.8  10.90  Epifaunal carn. inverts Sum  28.3 3.64  12.80 166.0  359.0  0.18  4.27 300.0  259.0  289.0  251.0  120.0  14.7  0.21 12.0  13.0  67.0  17.3 277.0  52.0  280.0  858.0  240.0  88.0  777.0  4408.0  80  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table D.2.3 EwE catch matrix for 2005 SE Misool model (kg—km-2)  Ad. groupers  0.35  0.71  0.35  1.77  1.77  0.35  17.68  Sub. groupers  0.34  0.68  0.34  1.70  1.70  0.34  16.98  0.49  Sum  Shrimp trawl  Foreign fleet  Lift net  Hook and line  Pole and line  Purse seine  Trolling  Blast fishing  Diving cyanide  Diving live fish  Diving spear  Portable trap  Permanent trap  Driftnet  Shore gillnet  Reef gleaning  Group  Spear and harpoon  Gear type  23.0 22.1  Juv. groupers  0.24  0.24  9.79  10.8  Ad. snappers  6.37  12.72  12.72  6.37  6.37  122.67  167.2  Sub. snappers  6.60  13.18  13.18  6.60  6.60  83.96  130.1  Juv. snappers  1.41  2.83  2.83  1.41  1.41  37.09  47.0  Ad. Napoleon wrasse  4.27  4.27  0.86  154.69  164.1  Sub. Napoleon wrasse  4.23  4.23  0.85  Juv. Napoleon wrasse  77.98  0.55  Skipjack tuna Other tuna  106.00  7.44  3.85  Mackerel Billfish  87.3  13.72 411.96  529.99  14.3  173.58  8.86  8.35  127.27  32.80  186.31  1407.8  4.79  33.3  164.02  324.1  119.70  Ad. coral trout  4.20  4.20  4.20  4.20  4.20  1.10  Juv. coral trout  0.50  0.50  0.50  0.50  0.50  0.13  119.7 22.1 2.6  Ad. large sharks  216.23  216.2  Juv. large sharks  23.99  24.0  Ad. small sharks  26.34  26.3  Juv. small sharks  2.91  Adult rays  14.05  Juv. rays  1.40  14.05 1.40  14.05 1.40  2.9  14.05  56.2  1.40  5.6  Ad. butterflyfish  4.16  4.16  4.16  4.16  4.16  0.74  21.9  Juv. butterflyfish  0.45  0.45  0.45  0.45  0.45  0.12  2.4  0.27  0.27  0.27  0.27  0.07  96.36  77.23  57.75  57.75  Cleaner wrasse Ad. large pelagic Juv. large pelagic  0.37  1.1 96.36  385.5  4.18  3.35  2.52  2.52  4.18  16.7  Ad. medium pelagic  16.53  13.25  9.91  9.91  16.53  66.1  Juv. medium pelagic  7.33  5.85  4.41  4.41  7.33  29.3  77.54  77.54  58.25  58.25  97.39  388.4  Ad. small pelagic Juv. small pelagic  19.42  8.63  8.63  6.45  6.45  2.16  Ad. large reef assoc.  120.88  120.88  120.88  120.88  110.37  31.83  432.12  1057.8  Juv. large reef assoc.  23.53  23.53  23.53  23.53  20.47  6.19  100.15  10.80  221.0  43.1  Ad. medium reef assoc.  14.30  14.30  14.30  14.30  13.23  3.77  53.05  127.2  Juv. medium reef assoc.  1.43  1.43  1.43  1.43  1.07  0.38  16.24  23.4 69.0  Ad. small reef assoc.  8.44  8.44  8.44  8.44  6.31  2.21  26.75  Juv. small reef assoc.  0.29  0.29  0.29  0.29  0.22  0.08  2.05  3.5  22.47  16.85  5.92  21.62  89.3  7.73  21.3  Ad. large demersal  22.47  Juv. large demersal  4.49  4.49  3.38  1.18  Ad. small demersal  14.26  14.26  14.26  3.76  Juv. small demersal  1.59  1.59  1.59  0.42  5.2  Ad. large planktivore  5.16  5.16  5.16  1.36  27.2 26.9  5.16  5.16  Juv. large planktivore  5.11  5.11  5.11  5.11  5.11  1.35  Ad. small planktivore  7.34  7.34  7.34  7.34  7.34  1.93  Juv. small planktivore  5.54  1.46  46.5  38.6  5.54  5.54  5.54  5.54  Ad. anchovy  308.08  246.38  184.69  184.69  447.27  1371.1  Juv. anchovy  34.24  27.38  20.55  20.55  34.24  137.0  29.2  Ad. deepwater fish  6.89  6.89  6.89  6.89  Juv. deepwater fish  0.77  0.77  0.77  0.77  Ad. macro algal browsing  2.88  2.88  2.88  2.88  0.38  11.9  Juv. macro algal browsing  0.28  0.28  0.28  0.28  0.07  1.2  Ad. eroding grazers  0.15  0.15  0.15  0.15  0.02  0.6  Juv. eroding grazers  0.01  0.01  0.01  0.01  0.00  0.0  Ad. scraping grazers  88.67  88.67  88.67  88.67  11.68  366.4  Juv. scraping grazers  8.61  8.61  8.61  8.61  2.25  36.7  Detritivore fish  0.39  0.39  0.39  0.39  0.05  1.6  27.5 3.1  Hermatypic corals  5.30  5.3  Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers  1608.48  1608.5  188.62  188.6  69.89 1.1E-02 4.91  3.4E-02  1.1E-02  69.9  5.7E-04  0.1  15.46  5.16  0.26  25.8  606.36  201.81  10.09  818.3  Large crabs  22.70  7.57  0.38  30.7  Small crabs  22.70  7.57  0.38  30.7  Giant triton  10.18  3.40  0.17  13.8  Herbivorous echinoids  22.70  7.57  0.38  30.7  Bivalves  65.50  Sessile filter feeders  11.11  Epifaunal det. inverts.  25.23  Lobsters  Epifaunal carn. inverts Sum  65.5 11.1 8.42  29.62 264.4  831.6  0.42  9.88 907.3  785.3  743.6  689.1  266.5  34.1  0.49 12.0  12.3  135.5  40.0 539.1  109.8  666.1  1678.5  948.0  191.1  1797.1  10577.3  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  81  Table D.2.4 EwE catch matrix for 2005 Kofiau model (kg•km-2)  Sum  Shrimp trawl  Foreign fleet  Lift net  Hook and line  Pole and line  Purse seine  Trolling  Blast fishing  Diving cyanide  Diving live fish  Diving spear  Portable trap  Permanent trap  Driftnet  Shore gillnet  Reef gleaning  Group  Spear and harpoon  Gear type  Ad. groupers  1.80  3.58  1.80  8.96  8.96  1.80  89.60  116.5  Sub. groupers  1.72  3.45  1.72  8.60  8.60  1.72  86.03  111.8  Juv. groupers  1.24  2.47  1.24  49.58  54.5  Ad. snappers  18.79  37.53  37.53  18.79  18.79  361.95  493.4  Sub. snappers  19.46  38.88  38.88  19.46  19.46  Juv. snappers  4.17  8.36  8.36  4.17  247.75  383.9  4.17  109.43  138.7  Ad. Napoleon wrasse  0.11  0.11  0.02  3.99  4.2  Sub. Napoleon wrasse  0.11  0.11  0.02  2.08  Juv. Napoleon wrasse  0.07  Skipjack tuna Other tuna  1.7  79.59  397.94  130.33  139.89  1057.1  2.69  1.39  3.20  3.01  1.73  12.0  23.65  6.10  Mackerel Billfish  2.3  1.67 309.32  30.48  60.2  285.00  Ad. coral trout  1.89  1.89  1.89  1.89  1.89  0.50  Juv. coral trout  0.23  0.23  0.23  0.23  0.23  0.06  285.0 10.0 1.2  Ad. large sharks  61.55  61.6  Juv. large sharks  6.83  6.8  Ad. small sharks  16.04  16.0  Juv. small sharks  1.77  1.8  Adult rays  21.10  21.10  21.10  21.10  Juv. rays  2.11  2.11  2.11  2.11  Ad. butterflyfish  2.53  2.53  2.53  2.53  2.53  Juv. butterflyfish  0.28  84.4 8.4 0.23  0.45  13.3  0.28  0.28  0.28  0.28  0.07  Cleaner wrasse  0.16  0.16  0.16  0.16  0.04  Ad. large pelagic  2.68  2.15  1.61  1.61  2.68  10.7  1.5  Juv. large pelagic  6.34  5.08  3.82  3.82  6.34  25.4  Ad. medium pelagic  6.90  5.53  4.14  4.14  6.90  27.6  Juv. medium pelagic  3.06  2.44  1.84  1.84  3.06  12.2  Ad. small pelagic  3.75  3.75  2.82  2.82  0.94  4.71  18.8  Juv. small pelagic  0.42  0.42  0.31  0.31  0.10  0.52  0.7  2.1  Ad. large reef assoc.  4.62  4.62  4.62  4.62  4.21  1.22  16.50  40.4  Juv. large reef assoc.  0.90  0.90  0.90  0.90  0.78  0.24  3.82  8.4  Ad. medium reef assoc.  0.54  0.54  0.54  0.54  0.50  0.14  2.01  4.8  Juv. medium reef assoc.  0.05  0.05  0.05  0.05  0.04  0.01  0.61  0.9  Ad. small reef assoc.  5.14  5.14  5.14  5.14  3.84  1.35  16.29  42.0  0.18  0.18  0.18  Juv. small reef assoc.  0.18  0.13  0.05  1.25  2.1  Ad. large demersal  16.87  16.87  12.65  4.45  16.23  67.1  Juv. large demersal  6.74  6.74  5.07  1.78  11.61  31.9  Ad. small demersal  9.02  9.02  2.38  Juv. small demersal  2.01  2.01  2.01  0.53  6.6  Ad. large planktivore  7.51  7.51  7.51  7.51  9.02  7.51  1.97  29.4 39.5  Juv. large planktivore  7.43  7.43  7.43  7.43  7.43  1.96  39.1  Ad. small planktivore  6.41  6.41  6.41  6.41  6.41  1.69  33.7  Juv. small planktivore  4.83  4.83  4.83  4.83  4.83  1.27  25.4  Ad. anchovy  60.45  48.34  36.24  36.24  87.76  269.0  Juv. anchovy  6.72  5.37  4.03  4.03  6.72  26.9  Ad. deepwater fish  0.31  0.31  0.31  0.31  Juv. deepwater fish  0.03  0.03  0.03  0.03  Ad. macro algal browsing  0.57  0.57  0.57  0.57  0.07  2.3  Juv. macro algal browsing  0.05  0.05  0.05  0.05  0.01  0.2  Ad. eroding grazers  0.07  0.07  0.07  0.07  0.01  0.3  Juv. eroding grazers  0.00  0.00  0.00  0.00  0.00  0.0  Ad. scraping grazers  42.47  42.47  42.47  42.47  5.59  175.5  Juv. scraping grazers  4.12  4.12  4.12  4.12  1.08  17.6  Detritivore fish  0.58  0.58  0.58  0.58  0.08  2.4  1.2 0.1  Hermatypic corals  3.23  3.2  Penaeid shrimps  2415.47  Shrimps and prawns  283.25  Squid Octopus Sea cucumbers  104.96 1.6E-02  5.1E-02  7.37  1.7E-02  2415.5 283.3 105.0  8.5E-04  8.5E-02  23.22  7.75  0.39  38.7  Lobsters  45.53  15.15  0.76  61.4  Large crabs  34.09  11.36  0.57  46.0  Small crabs  34.09  11.36  0.57  46.0  Giant triton  15.29  5.11  0.25  20.7  Herbivorous echinoids  34.09  11.36  0.57  46.0  Bivalves  98.36  Sessile filter feeders  16.68  Epifaunal det. inverts.  37.88  12.65  0.63  Epifaunal carn. inverts  44.49  14.84  0.74  Sum  131.7  383.8  98.4 16.7  289.2  187.2  297.8  195.8  135.6  17.8  18.0  83.0  51.2 60.1 597.0  81.0  424.8  1246.0  254.1  141.6  2698.7  7183.1  82  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Sum  14.4  15.8  IUU total  0.4  Shrimp trawl  0.7  Foreign fleet  35.1  0.4  Lift net  69.1  27.0  Hook and line 53.2  0.5  Pole and line  Blast fishing 1.1  2.7  Purse seine  Diving cyanide 5.3  2.7  Trolling  Diving live fish 5.3  0.5  Portable trap  Diving spear  Permanent trap  1.1  1.1  Driftnet  2.1  0.5  Shore gillnet  1.1  Reef gleaning  Group name Ad. groupers Sub. groupers Juv. groupers Ad. snappers Sub. snappers Juv. snappers Ad. Napoleon wrasse Sub. Napoleon wrasse Juv. Napoleon wrasse Skipjack tuna Other tuna Mackerel Billfish Ad. coral trout Juv. coral trout Ad. large sharks Juv. large sharks Ad. small sharks Juv. small sharks Adult rays Juv. rays Ad. butterflyfish Juv. butterflyfish Cleaner wrasse Ad. large pelagic Juv. large pelagic Ad. medium pelagic Juv. medium pelagic Ad. small pelagic Juv. small pelagic Ad. large reef assoc. Juv. large reef assoc. Ad. medium reef assoc. Juv. medium reef assoc. Ad. small reef assoc. Juv. small reef assoc. Ad. large demersal Juv. large demersal Ad. small demersal Juv. small demersal Ad. large planktivore Juv. large planktivore Ad. small planktivore Juv. small planktivore Ad. anchovy Juv. anchovy Ad. deepwater fish Juv. deepwater fish Ad. macro algal browsing Juv. macro algal browsing Ad. eroding grazers Juv. eroding grazers Ad. scraping grazers Juv. scraping grazers Detritivore fish Hermatypic corals Penaeid shrimps Shrimps and prawns Squid Octopus Sea cucumbers Lobsters Large crabs Small crabs Giant triton Herbivorous echinoids Bivalves Sessile filter feeders Epifaunal det. inverts. Epifaunal carn. inverts  Spear and harpoon  Table D.2.5 IUU catch for Raja Ampat (kg•km-2)  3.2  6.4  6.4  3.2  3.2  61.7  84.2  3.2  6.4  6.4  3.2  3.2  40.8  63.2  0.7  1.4  1.4  0.7  0.7  18.6  23.6  0.1  21.3  22.6  0.1  10.8  12.1  0.1  2.6  0.6  0.6  0.6  0.6  76.0  19.6  4.9 16.0  2.7  98.1  32.1  34.5  260.2  2.5  5.8  5.5  3.2  22.0  4.1  20.6  7.3  48.0  37.4 0.9  0.9  0.9  0.9  0.9  0.2  0.1  0.1  0.1  0.1  0.1  0.0  8.2 0.8  30.5  1.3  1.3  1.3  1.3  0.1  0.1  0.1  0.1  8.2  8.2  8.2  8.2  37.4 4.6 0.5 19.4  19.4  2.2  2.2  4.3  4.3  0.5  0.5 5.3 0.5  2.2  43.3 4.3  0.8  0.8  0.8  0.8  0.2  0.5  0.5  0.5  0.5  0.1  5.8  4.7  3.5  3.5  5.8  23.3  0.8  0.6  0.5  0.5  0.8  3.1  1.3  1.0  0.8  0.8  1.3  5.2  0.6  0.5  0.3  0.3  0.6  2.3  5.8  5.8  4.3  4.3  1.4  7.2  28.8  2.3  0.6  0.6  0.5  0.5  0.2  30.5  30.5  30.5  27.8  8.0  108.5  0.8  3.2 266.1  5.9  5.9  5.9  5.9  5.2  1.6  25.3  55.7  16.8  16.8  16.8  16.8  15.5  4.4  62.2  149.3  1.7  1.7  1.7  1.7  1.3  0.4  19.1  27.5  9.9  9.9  9.9  9.9  7.4  2.6  31.5  81.1  1.0  1.0  1.0  1.0  0.7  0.3  7.0  11.9  4.9  4.9  3.6  1.3  4.7  19.3  1.0  1.0  0.7  0.3  1.7  8.7  8.7  8.7  2.3  28.4 3.2  1.0  4.6  1.0  1.0  0.3  1.0  1.0  1.0  1.0  1.0  0.3  5.4  4.4  4.4  4.4  4.4  4.4  1.2  23.2  2.1  2.1  2.1  2.1  2.1  0.5  10.9  3.1  3.1  3.1  3.1  3.1  0.8  73.6  58.9  44.1  44.1  106.9  327.6  8.2  6.5  4.9  4.9  8.2  32.7  2.1  2.1  2.1  2.1  0.2  0.2  0.2  0.2  0.5  0.5  0.5  0.5  0.1  2.3  0.1  0.1  0.1  0.1  0.0  0.2  0.2  0.2  0.2  0.2  0.0  0.8  6.4E-03 6.4E-03 6.4E-03 6.4E-03  1.7E-03  2.7E-02  16.5  8.3 0.9  15.1  15.1  15.1  15.1  2.0  62.2  1.5  1.5  1.5  1.5  0.4  6.2  1.3  1.3  1.3  1.3  0.2  5.3  1.0  1.0 550.2  550.2  64.5  64.5  23.9 2.3E-03 7.4E-03  0.9  2.5E-03  1.2E-04  23.9 1.2E-02  2.8  0.9  0.0  4.7  97.4  32.5  1.6  131.5  7.8  2.6  0.1  10.5  7.8  2.6  0.1  10.5  1.8  0.6  0.0  2.5  7.8  2.6  0.1  10.5  22.4  22.4  3.8  3.8  8.7  2.9  10.1 111.9  170.4  0.1  3.4 220.1  187.8  201.9  174.3  56.8  11.7  0.2 9.2  9.2  44.0  13.7 134.3  26.3  124.6  574.1  155.5  44.9  614.7  2859.9  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  83  D.3. TROPHIC INTERACTION MATRICES Table D.3.1 Functional group diet composition. Raja Ampat 1990, Raja Ampat 2005, Kofiau Is., Dampier St., SE Misool Predator  Prey  RA 1990  RA 2005  Kofiau  Dampier  Misool  RA 1990  RA 2005  Kofiau  Dampier  Mysticetae  Juv. medium pelagic  1.65  1.70  0.50  1.00  1.70  Epifaunal carn. inverts  13.07  13.10  13.07  13.07  13.10  Juv. small pelagic  4.53  4.50  0.01  4.54  4.50  Infaunal inverts.  13.07  13.10  22.22  22.22  13.10  Squid  13.40  13.40  11.32  10.75  13.40  2.84  2.69  Octopus  Pisc. odontocetae  Crocodiles  20.29  20.20  20.29  20.29  20.30  6.06  6.11  6.05  6.05  6.10  21.11  20.05  20.00  Reef assoc. turtles  1.21  1.20  1.21  1.21  1.20  Large herb. zooplankton  40.21  40.20  21.23  20.16  40.20  Green turtles  1.21  1.20  1.21  1.21  1.20  Small herb. zooplankton  20.21  20.20  42.98  40.81  20.20  Oceanic turtles  1.21  1.20  1.21  1.21  1.20  Skipjack tuna  2.38  2.47  3.09  2.38  2.30  Juv. large pelagic  5.49  5.51  5.48  5.48  5.50 12.10  Ad. large pelagic  0.70  0.72  1.25  0.70  0.70  Juv. small pelagic  12.11  12.11  12.10  12.10  Juv. large pelagic  1.50  1.55  0.03  0.30  1.50  Juv. large planktivore  12.11  12.11  12.10  12.10  12.10  Ad. medium pelagic  0.10  0.10  0.17  0.10  0.10  Juv. small planktivore  12.11  12.11  12.10  12.10  12.10 12.10  Juv. medium pelagic  1.00  1.03  0.51  1.00  1.00  Penaeid shrimps  12.11  12.11  12.10  12.10  Ad. small pelagic  2.00  2.06  < 1E-2  2.00  2.00  Lobsters  8.02  8.01  5.61  5.61  8.00  Juv. small pelagic  20.00  20.61  0.37  19.98  20.00  Large crabs  3.35  3.30  3.01  3.01  3.30  0.34  0.33  Ad. groupers  0.20  0.20  < 1E-2  0.18  Sub. groupers  0.01  0.01  0.02  0.01  0.01  Juv. groupers  < 1E-2  0.10  0.10  0.10  0.05  Ad. large demersal  0.50  0.52  < 1E-2  0.35  0.50  Ad. small demersal  10.00  10.30  0.09  5.00  5.00  22.91  12.93  41.41  23.35  Bivalves Ad. groupers  28.40  0.20  23.37  24.11  Juv. small planktivore  14.77  15.25  0.42  15.16  14.80  Ad. snappers  0.10  0.01  0.02  0.01  0.01  Ad. deepwater fish  10.00  7.16  5.50  3.10  10.00  Sub. snappers  0.19  0.10  0.14  0.05  0.05  Squid  13.68  14.12  19.40  10.93  13.70  Juv. snappers  4.84  2.73  Ad. large pelagic  0.50  0.50  0.51  0.51  0.50  Ad. butterflyfish  0.60  0.20  0.07  2.02  Juv. large pelagic  0.50  0.50  0.10  0.10  0.50  Juv. butterflyfish  < 1E-2  0.60  < 1E-2  0.25  0.30  Juv. large demersal  3.00  3.03  1.52  1.52  2.00  Cleaner wrasse  0.05  0.05  < 1E-2  0.20  0.20  0.81  Juv. large sharks  5.00 2.00  Juv. deepwater fish  9.70  8.82  3.94  3.94  9.70  Juv. medium pelagic  0.10  Squid  25.58  25.85  20.80  20.80  26.60  Ad. large reef assoc.  3.00  1.51  2.03  0.71  9.90  Octopus  9.70  9.79  15.05  15.05  9.70  Juv. large reef assoc.  1.00  1.01  1.23  0.43  11.90  Epifaunal det. inverts.  15.24  15.35  10.84  10.84  15.20  Ad. medium reef assoc.  5.00  9.67  1.75  0.61  5.50  Epifaunal carn. inverts  29.09  29.38  29.56  29.56  29.10  Juv. medium reef assoc.  1.00  5.04  0.24  0.35  4.10  4.65  4.65  Ad. small reef assoc.  3.00  1.41  < 1E-2  10.60  1.40  Juv. small reef assoc.  2.00  Ad. large demersal  0.20  0.20  0.02  < 1E-2  0.04  Ad. small demersal  2.00  0.50  0.02  0.01  0.50  Ad. large planktivore  4.00  2.52  8.68  3.03  3.00  Infaunal inverts. Dugongs  Sea grass  100.00  100.00  100.00  100.00  100.00  Birds  Mackerel  0.18  0.20  0.20  0.27  0.20  Ad. small pelagic  0.10  Juv. small pelagic  0.10  Ad. small planktivore  0.70  1.98  1.42  0.90 0.50  Juv. large planktivore  0.14  0.05  0.50  Ad. small planktivore  0.10  1.01  < 1E-2  0.05  Ad. anchovy  2.50  4.03  2.36  1.43  0.60  2.00  2.92  5.79  2.02  2.90  0.20  Juv. small planktivore  0.60  0.50  0.63  5.39  Ad. anchovy  3.00  3.00  1.00  20.57  Juv. anchovy  0.50  7.00  17.58  12.59  25.50  Juv. anchovy Ad. deepwater fish  4.00  0.70  0.45  0.15  1.00  2.00  6.30  4.52  2.00  Juv. deepwater fish  1.00  0.91  0.59  0.20  1.00  0.20  0.71  0.05  0.11  2.10  0.09  6.06  0.08  0.26  0.71  Bivalves  7.00  Sessile filter feeders  1.50  Epifaunal det. inverts.  0.10  0.10  4.55  3.25  0.50  Ad. macro algal browsing  Epifaunal carn. inverts  14.90  5.00  18.57  13.30  5.00  Juv. macro algal browsing  Infaunal inverts.  13.01  1.00  3.52  2.52  1.00  Ad. eroding grazers  0.50  Juv. eroding grazers  Jellyfish and hydroids  0.50  2.40  4.03 0.50 2.20  0.10  Fishery discards  0.10  0.10  4.90  7.00  0.30  Ad. scraping grazers  Penaeid shrimps  4.43  4.41  5.32  5.32  4.40  Juv. scraping grazers  Shrimps and prawns  4.43  4.41  3.01  3.01  4.40  Detritivore fish  0.05  0.01  < 1E-2  0.28  0.10  0.53  0.53  Penaeid shrimps  1.00  8.06  10.44  3.63  5.60  Octopus  2.01  7.09  5.04  8.00  14.00  38.43  0.40  Sea cucumbers  11.08  11.12  11.08  11.08  11.10  Shrimps and prawns  1.00  8.66  5.91  2.06  4.60  Large crabs  0.24  0.20  0.21  0.21  0.20  Squid  0.23  0.10  0.33  0.11  0.10  Small crabs  0.23  0.20  0.20  0.20  0.20  Octopus  0.14  0.10  1.51  0.53  0.10  Herbivorous echinoids  3.50  3.51  2.80  2.80  3.50  Lobsters  0.10  0.30  0.09  0.04  0.30  0.76  0.76  Large crabs  0.20  0.81  0.26  0.09  0.30  Sessile filter feeders  60.58  60.72  48.46  48.46  60.80  Small crabs  1.00  1.11  1.23  0.43  1.10  Epifaunal det. inverts.  4.43  4.41  3.10  3.10  4.40  Giant triton  0.01  0.10  0.12  0.04  0.40  Epifaunal carn. inverts  5.54  5.51  5.54  5.54  5.50  Bivalves  0.14  0.20  0.69  0.24  0.10  Infaunal inverts.  5.54  5.51  18.99  18.99  5.50  Epifaunal det. inverts.  3.00  1.01  1.41  0.49  3.80  Jellyfish and hydroids  8.00  8.01  8.00  8.00  8.00  Epifaunal carn. inverts  17.90  9.97  13.30  4.57  4.50  Carn. zooplankton  1.02  1.00  1.02  1.02  1.00  Infaunal inverts.  15.81  13.09  15.14  5.26  4.00  Large herb. zooplankton  1.02  1.00  0.51  0.51  1.00  Carn. zooplankton  11.47  11.08  18.38  6.39  6.40  0.51  0.51  Ad. groupers  0.13  0.10  0.13  0.11  0.10 0.05  Bivalves  Small herb. zooplankton  Oceanic turtles  Jellyfish and hydroids Birds  20.00  Octopus  Green turtles  Misool  20.00  Juv. large planktivore  Reef assoc. turtles  Prey  Carn. zooplankton  Ad. large planktivore  Deep. odontocetae  Predator  Sub. groupers  Macro algae  38.85  38.84  38.84  38.84  38.80  Sub. groupers  0.06  0.05  0.06  0.05  Sea grass  51.11  51.15  51.12  51.11  51.20  Juv. groupers  0.05  0.20  0.25  0.20  0.10  Sea cucumbers  13.07  13.10  13.07  13.07  13.10  Ad. snappers  0.01  0.01  0.01  0.01  < 1E-2  Large crabs  0.62  0.60  0.55  0.55  0.60  Sub. snappers  0.24  0.10  0.12  0.10  0.10  Small crabs  0.68  0.70  0.58  0.58  0.70  Juv. snappers  0.16  0.16  Ad. butterflyfish  1.00  0.40  < 1E-2  2.00  1.90  Sessile filter feeders  26.14  26.10  20.91  20.91  26.10  Juv. butterflyfish  0.23  0.10  0.25  0.20  0.20  Epifaunal det. inverts.  13.07  13.10  9.15  9.15  13.10  Cleaner wrasse  0.27  0.20  < 1E-2  0.24  0.20  Bivalves  0.10  84  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table D.3.1. Cont. Functional group diet composition. Predator  Prey Ad. large reef assoc.  RA 1990  RA 2005  Kofiau  Dampier  Misool  3.97  3.54  1.70  1.40  3.30  Prey  RA 1990  RA 2005  Bivalves  Kofiau  Dampier  0.44  0.40  Misool  Juv. large reef assoc.  2.27  3.03  2.15  1.78  24.30  Epifaunal det. inverts.  7.01  5.03  0.77  0.70  4.10  Ad. medium reef assoc.  4.53  7.28  0.92  0.75  6.90  Epifaunal carn. inverts  5.55  5.64  1.10  1.00  5.80  Juv. medium reef assoc.  20.60  18.09  1.17  0.70  7.50  Infaunal inverts.  9.00  8.76  1.10  1.00  5.50  Ad. small reef assoc.  0.71  0.61  0.76  0.63  0.60  Carn. zooplankton  15.93  6.84  11.02  10.01  5.30  Juv. small reef assoc.  4.08  3.03  6.07  5.00  5.70  Large herb. zooplankton  11.02  10.01  Ad. large demersal  0.02  0.02  0.01  0.01  0.02  Small herb. zooplankton  11.02  10.01  Ad. small demersal  1.50  1.41  0.01  0.01  1.20  Phytoplankton  5.50  5.00  Ad. large planktivore  3.74  6.06  3.65  3.00  3.10  0.31  0.25  Juv. large planktivore  Ad. snappers  Ad. groupers  0.25  0.20  < 1E-2  0.24  0.10  Sub. groupers  0.01  0.05  < 1E-2  0.05  0.01  Ad. small planktivore  1.31  1.21  0.08  0.25  1.10  Juv. groupers  0.01  0.10  < 1E-2  0.10  0.01  Ad. anchovy  2.00  1.31  5.53  2.60  1.20  Ad. snappers  0.20  0.10  1.04  0.50  0.50  Juv. anchovy  1.00  5.36  6.58  5.43  5.10  Sub. snappers  0.30  0.30  0.86  0.20  0.20  Ad. deepwater fish  3.42  1.60  1.06  0.87  2.80  Juv. snappers  0.12  0.20  0.30  0.07  0.40  Juv. deepwater fish  3.73  0.91  1.59  1.32  3.10  Juv. Napoleon wrasse  0.10  0.05  < 1E-2  < 1E-2  0.05  Ad. macro algal browsing  0.10  0.71  0.11  0.09  0.09  Skipjack tuna  0.05  0.05  0.22  0.05  0.05  Ad. eroding grazers  1.16  0.02  0.02  0.02  0.02  0.12  0.80  Juv. eroding grazers Ad. scraping grazers  3.00  4.95  Juv. scraping grazers  Juv. groupers  Predator  6.07  5.00  34.18  44.76  4.80  Other tuna  0.59  0.30  < 1E-2  0.27  0.10  Mackerel  0.42  0.40  < 1E-2  0.40  0.20  Billfish  0.09  0.09  0.40  0.09  0.09  Juv. coral trout  0.01  0.01  0.22  0.05  0.80 0.09  Detritivore fish  0.15  0.05  < 1E-2  0.13  0.05  Juv. rays  0.09  0.09  0.19  0.09  Penaeid shrimps  7.25  6.47  4.26  3.50  6.10  Ad. butterflyfish  0.30  0.50  0.05  2.88  0.50  Shrimps and prawns  2.91  2.53  2.07  1.71  2.40  Juv. butterflyfish  0.01  0.01  0.02  1.00  0.90  Squid  0.19  0.10  0.17  0.14  0.20  Cleaner wrasse  0.01  0.20  < 1E-2  0.20  0.10  Octopus  0.19  0.20  0.61  0.51  0.20  Ad. large pelagic  0.60  0.02  0.08  0.02  0.02  Lobsters  0.13  0.10  0.09  0.08  0.10  Juv. large pelagic  0.10  0.20  < 1E-2  0.01  0.20  Large crabs  0.14  0.10  0.13  0.11  0.10  Ad. medium pelagic  0.05  0.05  0.01  0.01  0.05  Small crabs  0.43  0.40  0.39  0.32  0.40  Juv. medium pelagic  0.30  Giant triton  0.06  0.10  0.09  0.08  0.30  Ad. small pelagic  1.03  0.60  < 1E-2  0.05  0.60  Bivalves  0.76  0.71  0.93  0.76  0.70  Ad. large reef assoc.  5.00  4.03  0.01  3.50  4.70 8.30  Epifaunal det. inverts.  3.59  2.45  1.28  1.05  3.00  Juv. large reef assoc.  1.00  3.02  0.08  1.52  Epifaunal carn. inverts  7.38  8.08  7.93  6.51  6.20  Ad. medium reef assoc.  3.00  3.43  5.21  1.82  2.40  Infaunal inverts.  7.55  7.98  3.06  2.52  2.00  Juv. medium reef assoc.  5.42  4.13  0.01  1.20  1.70  Carn. zooplankton  10.17  10.31  6.07  5.00  4.80  Ad. small reef assoc.  2.14  2.11  < 1E-2  2.08  2.10  Ad. groupers  0.03  0.03  0.03  0.03  0.03  Juv. small reef assoc.  3.00  0.07  0.30  0.07  0.07  Sub. groupers  0.10  0.10  < 1E-2  0.10  0.10  Ad. large demersal  0.09  0.09  < 1E-2  < 1E-2  0.09  Juv. groupers  0.10  0.10  0.11  0.10  0.10  Juv. large demersal  0.07  0.07  0.11  0.02  0.07  Ad. snappers  0.05  0.05  0.05  0.05  0.05  Ad. small demersal  0.57  0.60  < 1E-2  0.08  0.20  Sub. snappers  0.10  0.10  0.11  0.10  0.10  Juv. small demersal  0.07  0.07  0.03  0.03  0.07  Ad. butterflyfish  1.80  0.81  < 1E-2  1.72  1.90  Ad. large planktivore  3.52  5.34  1.62  3.00  3.40  Juv. butterflyfish  0.10  0.05  0.11  0.10  0.10  Juv. large planktivore  0.08  0.07  2.48  0.57  0.07  Cleaner wrasse  0.42  0.40  0.44  0.40  0.40  Ad. small planktivore  < 1E-2  3.72  0.03  0.50  1.00  Ad. large reef assoc.  3.45  3.42  2.54  2.31  3.50  Juv. small planktivore  0.11  0.10  < 1E-2  0.11  0.10  Juv. large reef assoc.  5.00  10.07  1.87  1.70  11.00  Ad. anchovy  1.00  8.06  0.22  1.68  0.80  Ad. medium reef assoc.  2.10  2.11  1.65  1.51  2.20  Juv. anchovy  1.00  3.42  0.03  3.51  3.40 0.50  Juv. medium reef assoc.  8.56  8.66  6.31  5.73  8.90  Ad. deepwater fish  3.67  1.00  0.40  1.07  Ad. small reef assoc.  1.05  1.11  1.10  1.00  1.10  Juv. deepwater fish  5.15  0.91  0.03  1.43  0.50  Juv. small reef assoc.  6.00  6.04  10.53  9.56  10.30  Ad. macro algal browsing  0.06  2.62  0.11  0.05  0.06  Ad. large demersal  0.02  0.02  0.01  0.01  0.02  Juv. macro algal browsing  0.05  0.20  0.11  0.03  0.05  Ad. small demersal  1.85  1.81  < 1E-2  < 1E-2  1.90  Ad. eroding grazers  0.02  0.02  < 1E-2  < 1E-2  3.60  Ad. large planktivore  4.25  4.23  4.47  4.06  4.30  Juv. eroding grazers  0.10  0.02  0.08  4.10  0.02  0.45  0.41  Ad. scraping grazers  1.00  2.01  13.48  27.91  19.50  Ad. small planktivore  0.86  0.91  0.11  0.41  0.90  Juv. scraping grazers  0.08  0.07  10.81  0.30  Ad. anchovy  1.50  2.11  5.33  4.85  2.20  Detritivore fish  < 1E-2  0.01  < 1E-2  0.47  0.05  Juv. anchovy  2.00  10.47  8.81  8.01  10.80  Penaeid shrimps  14.31  5.44  < 1E-2  3.13  2.60  Ad. deepwater fish  1.33  0.91  0.42  0.38  1.30  Shrimps and prawns  2.68  4.84  1.77  2.60  Juv. deepwater fish  6.37  2.72  2.68  2.44  3.10  Squid  1.00  4.03  6.94  1.60  2.00  Ad. macro algal browsing  0.02  1.11  0.02  0.02  0.02  1.10  8.01  4.53  2.19  2.00  2.01  3.30  3.00  Juv. large planktivore  Juv. eroding grazers Ad. scraping grazers  4.47  Juv. scraping grazers  4.70  Octopus  0.12  0.20  3.13  0.83  0.10  Sea cucumbers  0.06  0.06  0.44  0.05  0.05  Lobsters  0.10  0.30  0.02  0.11  0.30  Large crabs  0.50  0.40  1.53  0.35  0.40  Detritivore fish  0.13  0.10  0.13  0.12  0.10  Small crabs  1.03  5.04  7.34  1.70  6.00  Penaeid shrimps  1.06  1.11  0.22  0.20  1.10  Crown of thorns  0.05  0.04  0.19  0.04  0.04  Shrimps and prawns  1.33  1.31  0.95  0.86  1.30  Giant triton  0.20  0.20  0.02  0.16  0.20  Squid  5.00  4.73  0.99  0.90  4.90  Herbivorous echinoids  0.06  1.41  0.19  0.04  0.05  0.61  0.55  Bivalves  1.54  1.61  8.50  1.95  1.60  Lobsters  0.10  0.10  0.07  0.07  0.10  Sessile filter feeders  8.19  9.06  0.35  6.37  8.00  Large crabs  0.10  0.10  0.10  0.09  0.10  Epifaunal det. inverts.  3.00  2.81  3.01  0.70  2.80  Small crabs  3.00  2.32  0.99  0.90  2.40  Epifaunal carn. inverts  3.66  3.32  4.28  1.00  8.30  Giant triton  0.26  0.10  0.22  0.20  0.30  Infaunal inverts.  6.77  6.67  20.15  4.64  3.40  Octopus  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  85  Table D.3.1. Cont. Functional group diet composition. Predator  Sub. snappers  Juv. snappers  RA 1990  RA 2005  Kofiau  Dampier  Misool  RA 1990  RA 2005  Kofiau  Dampier  Misool  Jellyfish and hydroids  Prey  1.26  0.70  1.19  0.27  0.30  Predator  Sub. snappers  Prey  0.47  0.40  2.33  0.43  0.40  Carn. zooplankton  6.52  2.42  9.65  2.22  2.00  Juv. snappers  0.80  Large herb. zooplankton  3.00  1.01  1.27  0.29  0.60  Juv. coral trout  Small herb. zooplankton  0.95  1.61  3.67  0.84  0.50  Ad. butterflyfish  < 1E-2  1.21  0.16  1.25  0.60  Ad. groupers  0.02  0.02  0.06  0.02  0.02  Juv. butterflyfish  0.01  0.30  0.01  0.30  1.00  0.70  Sub. groupers  0.05  0.10  < 1E-2  0.10  0.01  Cleaner wrasse  0.07  0.07  < 1E-2  0.07  0.07  Ad. snappers  0.10  0.10  0.29  0.10  0.10  Ad. large reef assoc.  3.63  3.52  0.02  1.00  3.50  Sub. snappers  0.50  0.10  1.41  0.51  0.50  Juv. large reef assoc.  1.03  3.52  0.14  1.26  3.50  Juv. snappers  1.14  0.50  0.26  0.09  0.09  Ad. medium reef assoc.  1.50  0.10  0.69  0.08  0.10  Juv. Napoleon wrasse  0.07  0.07  < 1E-2  < 1E-2  0.07  Juv. medium reef assoc.  0.51  0.50  < 1E-2  0.35  0.50  Other tuna  0.20  0.04  < 1E-2  0.04  0.04  Ad. small reef assoc.  0.80  0.80  < 1E-2  0.79  0.80  Mackerel  0.10  0.20  < 1E-2  0.17  0.20  Juv. small reef assoc.  3.85  1.00  2.00  5.90  1.00  Juv. coral trout  0.01  0.01  0.06  0.02  0.20  Ad. large demersal  0.02  0.02  < 1E-2  0.01  0.02  Juv. rays  0.16  0.10  0.12  0.08  0.01  Ad. small demersal  0.32  0.30  < 1E-2  0.01  0.20  Ad. butterflyfish  0.50  0.80  0.32  1.36  0.60  Ad. large planktivore  0.94  0.91  2.12  0.93  0.90  Juv. butterflyfish  0.01  0.40  0.01  0.40  1.10  Juv. large planktivore  2.37  0.52  Cleaner wrasse  0.09  0.09  < 1E-2  0.09  0.09  Ad. small planktivore  1.06  1.01  < 1E-2  0.52  1.00  Ad. large pelagic  0.85  < 1E-2  0.01  < 1E-2  < 1E-2  Ad. anchovy  0.10  3.11  0.46  0.05  0.10  Juv. large pelagic  0.01  0.01  < 1E-2  0.01  0.01  Juv. anchovy  2.00  0.10  1.00  0.11  0.10  Ad. medium pelagic  0.10  0.10  0.03  0.01  0.10  Ad. deepwater fish  1.11  0.77  0.75  0.33  0.50  Juv. medium pelagic  0.10  Juv. deepwater fish  0.95  0.82  0.01  0.37  0.50  Ad. small pelagic  0.50  0.50  0.01  0.05  1.10  Ad. macro algal browsing  0.03  0.03  0.02  0.03  0.03  Ad. large reef assoc.  4.53  5.02  1.46  3.21  4.50  Ad. eroding grazers  0.01  0.01  0.09  0.01  0.01  Juv. large reef assoc.  2.00  4.32  0.01  0.82  4.30  Ad. scraping grazers  0.01  2.92  4.54  1.00  8.70  Ad. medium reef assoc.  2.00  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.10  < 1E-2  0.13  0.05  4.53  Detritivore fish  Juv. medium reef assoc.  0.96  1.00  1.88  0.68  1.00  Penaeid shrimps  7.75  4.63  Ad. small reef assoc.  1.61  5.02  1.13  1.63  0.80  Shrimps and prawns  2.00  5.73  0.50  4.60  0.50  5.70  Juv. small reef assoc.  9.38  2.01  1.59  15.81  2.00  Octopus  0.20  0.69  Ad. large demersal  0.03  0.03  0.01  0.02  0.03  Large crabs  0.11  0.10  0.89  0.10  0.10  Juv. large demersal  0.09  0.09  0.09  0.03  0.09  Small crabs  0.48  0.50  3.67  0.40  0.40  Ad. small demersal  1.17  1.21  < 1E-2  0.09  0.30  Bivalves  0.75  0.08  Juv. small demersal  0.09  0.09  0.04  0.03  0.09  Epifaunal det. inverts.  0.50  Ad. large planktivore  3.52  4.02  1.24  3.56  3.50  Epifaunal carn. inverts  0.10  Juv. large planktivore  0.10  0.09  1.50  1.09  0.09  Infaunal inverts.  4.89  15.67  Ad. small planktivore  0.10  2.51  < 1E-2  1.00  1.00  Carn. zooplankton  65.47  51.12  Juv. small planktivore  0.94  0.90  0.04  0.95  5.00  Large herb. zooplankton  Ad. anchovy  1.00  4.02  1.38  0.50  1.00  Juv. anchovy  1.00  1.00  < 1E-2  1.11  1.00  Ad. deepwater fish  3.00  0.70  0.22  0.97  0.50  Juv. deepwater fish  3.37  0.90  0.02  1.29  Ad. macro algal browsing  0.02  4.52  0.04  Juv. macro algal browsing  0.05  0.05  Ad. eroding grazers  < 1E-2  < 1E-2  Juv. eroding grazers  0.10  0.02  Ad. scraping grazers  1.00  4.32  Juv. scraping grazers  0.09  0.60  57.15  3.36  10.00  Small herb. zooplankton  0.71  10.00  Phytoplankton  46.96  5.00  64.80  Ad. groupers  0.13  0.10  0.13  0.13  0.10  0.20  Sub. groupers  0.10  0.50  0.49  0.50  0.10  0.02  0.02  Juv. groupers  0.02  0.10  0.13  0.13  0.10  1.52  0.05  Ad. snappers  0.50  0.60  0.98  0.99  1.00  0.01  < 1E-2  < 1E-2  Sub. snappers  1.56  1.61  1.52  1.55  1.60  0.12  0.04  0.02  Juv. snappers  0.61  0.50  0.13  0.13  4.10  7.18  18.16 6.20  Ad. Napoleon wrasse  21.72  Ad. Napoleon wrasse  0.10  0.30  0.19  0.40  0.60  Sub. Napoleon wrasse  1.00  0.60  0.63  0.64  0.60 0.10  Detritivore fish  < 1E-2  0.10  < 1E-2  0.97  0.05  Juv. Napoleon wrasse  0.50  0.10  0.13  0.13  Penaeid shrimps  7.19  10.14  7.29  7.92  7.20  Juv. coral trout  0.01  0.01  0.32  0.13  Shrimps and prawns  3.19  3.21  1.50  2.19  3.20  Juv. rays  0.13  0.10  0.13  0.13  0.20  Squid  2.00  2.21  4.45  1.60  2.00  Ad. butterflyfish  0.77  0.80  0.75  0.76  0.80  Octopus  0.14  0.20  2.58  0.93  6.00  Juv. butterflyfish  0.10  1.11  1.12  1.14  1.10  Sea cucumbers  0.07  0.07  0.19  0.07  0.07  Ad. large reef assoc.  0.64  0.60  0.44  0.44  0.60  Lobsters  0.21  0.20  0.01  0.15  0.20  Juv. large reef assoc.  0.77  1.51  0.51  0.52  0.80  Large crabs  0.18  0.20  0.46  0.17  0.20  Ad. medium reef assoc.  1.00  0.80  0.56  0.57  0.80  Small crabs  0.55  0.60  1.30  0.47  0.60  Juv. medium reef assoc.  4.84  0.03  0.02  0.02  0.03  Crown of thorns  0.05  0.05  0.14  0.05  0.05  Ad. small reef assoc.  2.64  2.62  2.57  2.62  2.60 0.10  Giant triton  0.20  0.50  0.03  0.10  0.20  Juv. small reef assoc.  3.00  0.10  0.13  0.13  Herbivorous echinoids  0.04  0.04  0.10  0.04  0.04  Ad. large demersal  0.28  0.30  0.19  0.19  0.30  Bivalves  1.85  1.81  5.57  2.02  1.80  Juv. large demersal  0.13  0.10  0.04  0.04  0.10  Sessile filter feeders  9.73  9.74  0.27  7.87  9.70  Ad. small demersal  8.00  6.44  1.88  1.91  2.40  Epifaunal det. inverts.  3.02  3.01  5.93  2.14  3.00  Juv. small demersal  0.13  0.10  0.05  0.05  0.10  Epifaunal carn. inverts  6.81  6.83  19.05  6.89  13.10  Ad. large planktivore  3.19  3.22  3.11  3.17  3.20  Infaunal inverts.  10.61  6.03  17.37  6.28  12.10  Juv. large planktivore  0.13  0.10  3.25  3.31  0.10  Jellyfish and hydroids  0.34  0.40  0.93  0.34  0.30  Ad. small planktivore  5.00  6.44  1.57  3.18  6.30  Carn. zooplankton  7.32  3.21  7.71  2.75  2.80  Juv. small planktivore  0.13  0.10  0.13  0.13  0.10  Large herb. zooplankton  0.67  0.70  0.93  0.34  0.70  Ad. anchovy  1.61  1.43  1.46  Small herb. zooplankton  0.67  5.02  3.35  1.02  0.70  Juv. anchovy  1.00  3.02  2.93  2.98  3.00  Ad. groupers  0.01  0.02  < 1E-2  0.02  0.02  Ad. deepwater fish  1.28  0.91  0.38  0.38  1.30  Sub. groupers  0.01  0.05  < 1E-2  0.05  0.01  Juv. deepwater fish  2.69  1.18  0.40  0.51  1.30  Ad. snappers  0.05  0.05  0.47  0.05  0.05  Ad. macro algal browsing  2.56  2.62  2.51  2.55  2.60  86  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table D.3.1. Cont. Functional group diet composition. Predator  RA 1990  RA 2005  Kofiau  Dampier  Misool  Juv. macro algal browsing  Prey  0.10  0.10  0.05  0.05  0.10  Predator  Prey  RA 1990  RA 2005  Kofiau  Dampier  Misool  Juv. Napoleon wrasse  Ad. groupers  0.01  0.01  < 1E-2  0.01  Ad. eroding grazers  1.00  0.10  0.13  0.13  0.01  0.10  Sub. groupers  0.10  0.40  0.34  0.37  Juv. eroding grazers  0.03  0.03  0.08  0.10  0.08  0.03  Ad. snappers  0.20  0.40  0.34  0.37  0.40  Ad. scraping grazers  0.64  0.60  Juv. scraping grazers  0.14  0.20  0.62  0.63  1.00  Sub. snappers  0.64  0.40  0.34  0.37  0.40  9.67  6.53  9.50  Ad. Napoleon wrasse  0.30  0.15  0.18  0.37  < 1E-2  0.40  0.50  0.75  0.77  0.50  Sub. Napoleon wrasse  0.37  0.40  0.34  0.37  0.40  Squid Octopus  1.00  9.64  2.00  2.04  2.60  Ad. butterflyfish  0.31  0.30  0.29  0.31  0.30  2.56  2.62  3.00  3.05  2.60  Juv. butterflyfish  0.10  0.30  0.29  0.31  0.30  Sea cucumbers  3.00  2.62  2.51  2.55  2.60  Ad. medium reef assoc.  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Lobsters  0.40  0.91  0.59  0.60  0.90  Ad. small reef assoc.  1.34  1.30  1.23  1.34  1.30  Large crabs  0.34  0.30  0.30  0.31  0.30  Ad. small demersal  0.86  0.60  0.17  0.18  0.60  Small crabs  2.59  2.62  2.16  2.19  2.60  Juv. large planktivore  0.19  0.21  Crown of thorns  1.00  8.25  7.98  8.12  8.10  Ad. small planktivore  0.42  0.40  0.10  0.21  0.40  Giant triton  0.20  0.30  2.02  2.06  1.00  Ad. deepwater fish  0.31  0.21  0.08  0.09  0.30  Herbivorous echinoids  0.85  5.73  10.00  10.18  5.90  Juv. deepwater fish  0.73  0.63  0.27  0.29  0.70  Bivalves  5.14  5.13  8.43  8.57  5.10  Ad. macro algal browsing  0.12  0.10  0.11  0.12  0.10  Sessile filter feeders  7.05  7.04  5.50  5.60  6.90  Ad. eroding grazers  0.02  0.02  0.02  0.02  0.02  Epifaunal det. inverts.  3.46  2.62  1.75  1.78  2.60  Ad. scraping grazers  0.10  0.10  0.09  0.10  0.10  Detritivore fish  Sub. Napoleon wrasse  Epifaunal carn. inverts  2.56  2.61  2.51  2.55  2.60  Juv. scraping grazers  9.18  1.08  Infaunal inverts.  9.40  2.62  4.63  4.71  2.60  Detritivore fish  0.10  0.10  0.09  0.10  0.40  Carn. zooplankton  10.53  7.23  6.60  6.61  6.30  Squid  1.22  1.20  0.90  0.98  1.20  Ad. groupers  0.14  0.10  0.16  0.15  0.10  Octopus  2.44  2.40  2.47  2.69  2.40  Sub. groupers  0.02  0.10  0.12  0.11  0.05  Sea cucumbers  3.00  6.10  5.62  6.11  6.10  Juv. groupers  < 1E-2  0.09  0.11  0.10  0.01  Lobsters  0.30  0.70  0.45  0.49  0.70  Ad. snappers  0.10  0.40  0.47  0.44  0.40  Large crabs  0.36  0.40  0.29  0.32  0.40  Sub. snappers  0.50  0.80  1.13  1.05  1.00  Small crabs  1.00  6.10  4.78  5.19  6.10  Juv. snappers  0.26  0.30  0.11  0.10  0.09  Crown of thorns  2.00  5.00  4.58  4.98  5.00 1.70  Ad. Napoleon wrasse  0.30  0.15  0.15  0.22  0.20  Giant triton  0.50  1.70  1.22  1.33  Sub. Napoleon wrasse  1.29  0.70  0.23  0.22  0.70  Herbivorous echinoids  2.00  7.61  3.67  4.00  7.60  Juv. Napoleon wrasse  0.09  0.09  0.03  0.10  0.09  Bivalves  6.09  6.10  0.92  1.00  6.10  Juv. coral trout  0.01  0.02  0.10  0.02  0.02  Sessile filter feeders  6.77  6.61  0.92  1.00  6.60  Juv. rays  0.12  0.09  0.11  0.10  0.09  Epifaunal det. inverts.  6.00  9.71  6.29  6.84  9.70  Ad. butterflyfish  0.37  0.80  0.94  0.88  0.80  Epifaunal carn. inverts  9.91  9.71  8.98  9.77  9.70  Juv. butterflyfish  < 1E-2  0.60  0.73  0.69  0.60  Infaunal inverts.  15.49  9.71  12.88  14.02  9.70  Juv. large reef assoc.  0.30  6.42  0.17  0.16  0.09  Carn. zooplankton  13.00  10.11  9.20  10.01  9.80  Ad. medium reef assoc.  0.58  0.60  0.50  0.48  0.60  Large herb. zooplankton  11.00  2.40  9.20  10.01  2.40  Juv. medium reef assoc.  5.00  3.01  0.02  0.02  0.02  Small herb. zooplankton  7.00  2.40  9.20  10.01  2.40  Ad. small reef assoc.  3.62  3.21  3.74  3.51  3.20  Phytoplankton  4.77  5.01  Juv. small reef assoc.  1.25  0.09  0.11  0.10  0.09  Detritus  5.90  6.20  Juv. large demersal  0.12  0.09  0.03  0.03  0.09  Skipjack tuna  3.00  0.70  0.38  0.69  1.50  Ad. small demersal  3.00  2.01  0.90  3.38  1.00  Other tuna  1.53  1.51  < 1E-2  1.00  0.30  Juv. small demersal  0.09  0.09  0.04  0.04  0.09  Mackerel  0.20  0.20  0.10  0.20  0.20  Juv. large planktivore  0.09  0.09  2.71  2.55  0.09  Ad. large pelagic  0.01  0.01  < 1E-2  0.01  0.01  Ad. small planktivore  1.00  4.52  0.51  2.46  2.00  Juv. large pelagic  0.02  0.02  0.02  0.02  0.02  Skipjack tuna  6.20  Juv. small planktivore  0.10  0.09  0.10  0.09  0.09  Ad. medium pelagic  0.10  0.10  < 1E-2  < 1E-2  < 1E-2  Ad. deepwater fish  0.68  0.49  0.24  0.22  0.70  Juv. medium pelagic  < 1E-2  0.40  0.10  < 1E-2  0.40  Juv. deepwater fish  0.68  0.63  0.32  0.30  0.70  Ad. small pelagic  0.02  0.02  0.02  0.02  0.02  Ad. macro algal browsing  0.27  0.40  0.31  0.29  0.30  Juv. small pelagic  2.00  0.40  0.20  0.38  0.40  Juv. macro algal browsing  0.10  0.10  0.03  0.03  0.05  Ad. large planktivore  0.09  0.20  6.18  11.90  0.09  Ad. eroding grazers  0.03  0.03  0.03  0.03  0.03  Juv. large planktivore  0.50  3.22  3.34  3.22  3.20  Juv. eroding grazers  0.02  0.02  0.05  0.05  0.02  Ad. small planktivore  0.12  0.10  0.03  0.06  0.10  Ad. scraping grazers  0.14  0.10  0.97  0.91  0.10  Juv. small planktivore  0.10  1.01  0.02  1.00  1.00 11.40  Juv. scraping grazers  0.10  4.21  17.90  Ad. anchovy  2.00  8.03  4.76  0.05  Detritivore fish  0.01  0.10  0.13  0.12  0.10  Juv. anchovy  0.10  0.10  1.14  1.10  0.10  Squid  1.50  2.71  2.52  2.37  2.70  Ad. deepwater fish  0.10  0.70  0.75  0.72  1.00  Octopus  2.72  3.81  3.79  3.56  2.70  Juv. deepwater fish  0.10  0.91  0.42  0.40  1.00  Sea cucumbers  2.00  5.42  6.32  5.93  5.40  Penaeid shrimps  1.48  3.43  1.57  1.51  1.50  Lobsters  0.02  0.40  0.31  0.29  0.40  Shrimps and prawns  0.15  0.10  0.11  0.10  0.10  Large crabs  0.60  0.60  0.60  0.56  0.60  Squid  0.06  0.06  0.05  0.05  0.06 0.20  Small crabs  1.00  5.42  5.38  5.04  5.40  Octopus  0.24  0.20  0.28  0.27  Crown of thorns  0.10  2.81  3.31  3.11  2.80  Lobsters  0.02  0.02  < 1E-2  0.01  0.02  Giant triton  0.30  0.50  0.03  1.60  0.10  Large crabs  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Herbivorous echinoids  1.00  2.01  12.66  11.87  4.00  Small crabs  0.01  0.01  < 1E-2  < 1E-2  0.01  Bivalves  8.15  8.14  14.09  13.22  8.10  Giant triton  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Sessile filter feeders  7.47  7.53  6.96  6.53  7.50  Bivalves  0.50  0.20  0.25  0.24  0.20  Epifaunal det. inverts.  3.00  5.42  4.42  4.15  5.40  Epifaunal det. inverts.  0.30  0.30  0.22  0.21  0.30  Epifaunal carn. inverts  6.00  5.41  6.32  5.93  5.40  Epifaunal carn. inverts  0.33  0.30  0.33  0.32  0.30  Infaunal inverts.  11.57  12.23  9.98  9.35  11.00  Infaunal inverts.  2.00  0.30  0.43  0.41  0.30  Carn. zooplankton  10.00  7.02  8.02  7.53  7.10  Carn. zooplankton  3.68  0.20  0.18  0.18  0.20  Large herb. zooplankton  10.00  Large herb. zooplankton  1.08  0.20  0.10  0.09  0.20  Bird’s Head Seascape Analyses: II, Bailey, M., Pitcher, T.J.  87  Table D.3.1. Cont. Functional group diet composition. Predator Other tuna  RA 1990  RA 2005  Kofiau  Dampier  Misool  Small herb. zooplankton  Prey  0.24  0.20  0.33  0.32  0.20  Skipjack tuna  2.00  Other tuna  RA 1990  RA 2005  Kofiau  Dampier  Ad. small planktivore  Prey  0.29  0.30  0.07  0.14  Misool 0.30  Juv. small planktivore  4.09  4.10  0.17  4.09  4.10 10.00  2.00  0.10  0.10  0.12  0.10  Ad. anchovy  8.00  10.00  7.32  3.72  Juv. large sharks  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. anchovy  4.00  7.60  9.15  8.58  7.60  Juv. small sharks  0.04  0.05  0.09  0.05  0.05  Ad. deepwater fish  0.03  0.02  < 1E-2  < 1E-2  0.03  Juv. deepwater fish  2.00  0.18  0.06  0.07  0.20  Ad. macro algal browsing  0.03  0.03  0.04  0.03  0.03  Juv. rays  0.01  0.01  < 1E-2  0.01  0.01  Ad. large pelagic  0.10  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. large pelagic  0.05  0.01  < 1E-2  0.01  0.01  Ad. eroding grazers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. medium pelagic  0.10  0.10  0.02  0.02  0.02  Ad. scraping grazers  0.12  0.10  0.11  0.11  0.40  Juv. medium pelagic  0.05  0.10  0.17  0.05  0.10  Juv. scraping grazers  4.57  0.61  Ad. small pelagic  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Detritivore fish  0.03  0.02  Juv. small pelagic  0.11  0.10  0.80  0.10  0.10  Penaeid shrimps  0.02  0.02  Juv. medium reef assoc.  2.71  Shrimps and prawns  0.08  0.08  0.06  0.05  0.08  Ad. large planktivore  0.01  0.01  8.31  9.49  0.01  Squid  3.70  0.10  0.09  0.09  0.10  Juv. large planktivore  0.10  3.70  3.28  3.75  3.70  Octopus  0.03  0.03  0.03  0.03  0.02  0.02  0.02  Ad. small planktivore  0.07  0.07  0.02  0.04  0.07  Carn. zooplankton  7.00  Juv. small planktivore  1.00  1.00  0.88  1.00  1.00  Large herb. zooplankton  5.00  Ad. anchovy  2.00  9.01  8.77  < 1E-2  10.00  Skipjack tuna  5.00  4.30  2.82  4.26  5.10  Juv. anchovy  0.01  0.01  4.39  1.01  0.01  Other tuna  7.53  2.50  < 1E-2  2.49  0.70 < 1E-2  Billfish  Ad. deepwater fish  1.00  0.35  0.13  0.15  0.50  Mackerel  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. deepwater fish  0.10  0.08  0.03  0.04  0.09  Billfish  2.00  0.30  0.30  0.29  0.30  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. large pelagic  0.10  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Penaeid shrimps  1.23  0.30  0.25  0.29  0.30  Ad. medium pelagic  0.60  0.03  < 1E-2  0.01  0.03  Shrimps and prawns  0.05  0.05  0.03  0.04  0.05  Ad. small pelagic  0.10  0.01  < 1E-2  0.01  0.01  Squid  0.05  0.05  0.03  0.04  0.05  Juv. small pelagic  0.20  0.20  1.00  0.20  0.20 1.10  Anemonies  Octopus  0.07  0.07  0.07  0.08  0.07  Ad. large planktivore  0.05  0.10  6.06  5.90  Sea cucumbers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. small planktivore  0.10  0.10  0.04  4.00  0.10  Lobsters  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. small planktivore  2.00  2.00  0.08  2.00  2.00  Large crabs  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. anchovy  5.00  10.01  2.35  0.05  10.00  Small crabs  0.01  0.01  < 1E-2  0.01  0.01  Juv. anchovy  0.20  0.10  4.71  1.10  0.10  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. deepwater fish  8.00  0.01  < 1E-2  < 1E-2  0.02  0.99  1.10  Crown of thorns Giant triton  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. deepwater fish  8.00  Herbivorous echinoids  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Penaeid shrimps  1.07  Shrimps and prawns  4.00  Squid  0.12  Bivalves  0.07  0.07  0.06  0.07  0.07  Sessile filter feeders  0.90  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Epifaunal det. inverts.  0.13  0.10  0.08  0.09  0.10  Octopus  0.45  0.43  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.10  0.09  0.09  0.10  0.03  0.02  Epifaunal carn. inverts  0.23  0.20  0.20  0.23  0.20  Lobsters  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Infaunal inverts.  4.78  0.30  0.26  0.30  0.30  Large crabs  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Jellyfish and hydroids  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Small crabs  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Carn. zooplankton  9.64  8.61  7.55  8.63  8.60  Giant triton  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Large herb. zooplankton  3.51  3.50  1.54  1.75  3.50  Bivalves  < 1E-2  < 1E-2  Small herb. zooplankton  Mackerel  Predator  0.44  0.40  1.92  2.19  0.40  Epifaunal det. inverts.  0.01  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Macro algae  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Epifaunal carn. inverts  0.02  0.02  0.02  0.02  0.02  Sea grass  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Infaunal inverts.  0.02  0.02  0.02  0.02  0.02  Ad. groupers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. groupers  0.11  0.10  0.13  0.11  0.10 0.01  Ad. coral trout  Sub. groupers  0.01  0.01  < 1E-2  0.01  0.01  Sub. groupers  0.20  0.20  < 1E-2  0.19  Ad. snappers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. snappers  0.68  0.71  0.80  0.66  0.70  Sub. snappers  0.03  0.03  0.03  0.03  0.03  Sub. snappers  2.93  2.93  3.45  2.84  2.90  Ad. Napoleon wrasse  0.30  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. snappers  Sub. Napoleon wrasse  0.01  0.01  < 1E-2  0.01  0.01  Ad. Napoleon wrasse  0.11  0.15  0.01  < 1E-2  0.10  3.50  Other tuna  0.57  0.60  0.18  0.57  0.30  Sub. Napoleon wrasse  0.11  0.10  0.06  0.05  0.10  Mackerel  0.89  0.90  0.46  0.89  0.90  Ad. butterflyfish  1.00  3.83  0.14  3.71  0.90  Ad. butterflyfish  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. butterflyfish  0.50  2.83  0.03  2.71  4.10  Juv. butterflyfish  0.40  0.40  0.39  0.40  0.40  Cleaner wrasse  0.18  0.20  0.21  0.17  0.10  Cleaner wrasse  0.01  0.01  < 1E-2  0.01  0.01  Juv. medium pelagic  1.80  Ad. large pelagic  0.14  0.10  0.14  0.14  0.10  Ad. large reef assoc.  20.28  20.48  5.58  13.75  15.00  Juv. large pelagic  0.13  0.10  0.13  0.13  0.10  Juv. large reef assoc.  15.00  19.07  7.82  6.46  18.90  Ad. medium pelagic  0.05  0.05  0.05  0.05  0.05  Ad. medium reef assoc.  0.70  0.71  0.62  0.51  0.70  Juv. medium pelagic  0.29  0.30  0.18  0.29  0.30  Ad. small reef assoc.  6.21  2.32  2.69  2.21  2.30  Ad. small pelagic  0.72  0.70  < 1E-2  0.72  0.70  Juv. small reef assoc.  6.79  10.90  12.69  10.48  1.00 0.20  Juv. small pelagic  1.09  1.10  2.00  1.09  1.10  Ad. large demersal  0.36  0.40  0.07  0.24  Ad. large reef assoc.  0.29  0.30  0.19  0.20  0.30  Ad. small demersal  0.57  0.61  0.20  0.17  0.30  Juv. large reef assoc.  1.72  1.70  0.58  0.60  1.70  Ad. large planktivore  3.27  3.33  3.84  3.17  3.30  Ad. medium reef assoc.  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. large planktivore  1.33  1.09  Juv. medium reef assoc.  0.17  0.20  0.11  0.12  0.20  Ad. small planktivore  2.26  2.32  Ad. small reef assoc.  0.20  0.20  0.19  0.20  0.20  Ad. anchovy  3.28  0.50  1.88  1.55  Ad. large demersal  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. anchovy  3.27  3.33  3.89  3.21  3.30  Ad. small demersal  0.06  0.06  0.02  0.02  0.06  Ad. deepwater fish  3.04  2.10  1.07  0.88  0.50  Ad. large planktivore  0.03  0.03  0.03  0.03  0.03  Juv. deepwater fish  3.04  2.73  1.43  1.18  0.50  6.06  6.24  Ad. macro algal browsing  0.11  0.10  0.13  0.11  0.10  Juv. large planktivore  1.00 0.80  88  Chapter 1 Ecosystem Simulation Models of Raja Ampat  Table D.3.1. Cont. Functional group diet composition. Predator  RA 1990  RA 2005  Kofiau  Dampier  Misool  Ad. eroding grazers  Prey  0.05  0.05  0.05  0.04  0.05  Juv. small sharks  Ad. scraping grazers  11.72  11.81  17.80  14.68  33.30  Whale shark  26.93  22.21  Manta ray  0.02  0.02  0.02  0.02  0.02  Detritivore fish  < 1E-2  1.82  < 1E-2  1.70  0.05  Adult rays  0.50  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Penaeid shrimps  1.01  1.01  1.43  1.18  1.00  Juv. rays  0.50  Shrimps and prawns  1.01  1.01  0.81  0.67  1.00  Ad. butterflyfish  0.41  0.40  0.37  0.40  0.40  Squid  2.30  2.32  2.14  1.78  2.20  Juv. butterflyfish  0.37  0.40  0.33  0.36  0.40  0.68  0.56  Cleaner wrasse  0.01  < 1E-2  < 1E-2  < 1E-2  0.01 0.03  Juv. scraping grazers  Octopus  RA 1990  RA 2005  Kofiau  Dampier  5.00  0.10  0.19  0.10  Misool 0.10  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Lobsters  0.16  0.20  0.13  0.11  0.20  Ad. large pelagic  0.04  0.03  0.03  0.03  0.03  0.03  0.03  0.02  0.03  Ad. medium pelagic  0.01  < 1E-2  < 1E-2  < 1E-2  0.01  Small crabs  0.09  0.09  0.09  0.07  0.09  Ad. small pelagic  0.03  0.03  0.03  0.03  0.03  Giant triton  0.19  0.20  0.18  0.15  0.20  Ad. large reef assoc.  3.08  4.07  5.57  2.11  3.10  0.06  0.05  Juv. large reef assoc.  5.47  5.45  1.74  1.88  5.50  0.37  0.31  < 1E-2  4.96  2.78  < 1E-2  < 1E-2  Epifaunal det. inverts.  0.45  0.50  0.50  Ad. medium reef assoc.  Epifaunal carn. inverts  4.98  0.50  0.53  0.44  0.50  Ad. small reef assoc.  0.22  0.20  1.96  2.11  0.20  Infaunal inverts.  1.45  0.50  0.69  0.57  0.50  Ad. large demersal  0.05  0.05  0.03  0.03  0.05  Ad. groupers  0.01  0.01  0.01  0.01  < 1E-2  Ad. small demersal  0.05  0.05  < 1E-2  0.01  0.05  Sub. groupers  0.15  0.10  0.19  0.15  0.05  Ad. large planktivore  0.76  0.79  0.70  0.75  0.80  Ad. snappers  0.15  0.10  0.19  0.15  0.10  Juv. large planktivore  0.15  0.16  Sub. snappers  0.30  0.30  0.39  0.30  0.30  Ad. small planktivore  0.32  0.30  0.07  0.16  0.20  Ad. anchovy  0.07  0.07  0.20  0.22  0.07  0.10  Juv. anchovy  0.46  0.50  0.43  0.46  0.50 0.90  Juv. snappers Ad. butterflyfish  0.15  0.10  Juv. butterflyfish  1.00  Cleaner wrasse  1.09  Ad. large reef assoc.  0.30  0.19  0.15  10.10  0.03  15.00  1.00  Ad. deepwater fish  0.90  0.62  0.24  0.26  1.12  1.40  1.09  0.30  Juv. deepwater fish  0.46  0.45  0.17  0.18  0.50  0.15  0.10  0.14  0.11  0.10  Ad. macro algal browsing  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. large reef assoc.  33.21  33.66  14.91  11.62  32.90  Ad. eroding grazers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. medium reef assoc.  < 1E-2  5.15  < 1E-2  < 1E-2  < 1E-2  Ad. scraping grazers  1.37  1.39  1.24  1.34  1.40  Juv. medium reef assoc.  7.30  7.40  6.57  5.11  7.20  Juv. scraping grazers  4.84  5.22  Detritivore fish  Ad. small reef assoc.  2.02  2.03  2.59  2.02  2.00  Ad. large demersal  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. small demersal  2.00  2.03  0.38  0.60  1.00  Penaeid shrimps  Ad. large planktivore  3.54  3.55  4.54  3.54  3.50  3.28  2.56  Juv. large planktivore  0.08  0.07  0.06  0.07  0.08  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.03  0.03  Shrimps and prawns  0.14  0.10  0.08  0.09  0.10  Squid  2.85  2.88  2.08  2.24  2.90  Anemonies  Ad. small planktivore  5.12  5.17  < 1E-2  2.56  1.00  Octopus  Ad. anchovy  0.74  0.71  8.36  6.51  0.70  Sea cucumbers  Juv. anchovy  15.00  13.49  17.14  13.36  13.20  Lobsters  Ad. deepwater fish  1.33  0.92  0.51  0.40  1.30  Large crabs  Juv. deepwater fish  6.50  5.39  3.02  2.35  0.50  Small crabs  Ad. macro algal browsing  0.10  0.10  0.19  0.15  0.10  Crown of thorns  Juv. macro algal browsing  6.53  6.59  1.40  3.27  7.40  Giant triton  Ad. eroding grazers  0.10  0.06  0.08  0.06  0.06  Herbivorous echinoids  0.52  3.27  Bivalves  1.15  0.12  Sessile filter feeders  0.39  0.40  0.89  0.96  0.40  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.04  0.04  0.03  0.03  0.04  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.01  0.01  < 1E-2  0.01  0.01  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.10  0.10  0.07  0.08  0.10  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Juv. eroding grazers  5.00  Ad. scraping grazers  0.12  0.10  30.59  23.82  25.30  Epifaunal det. inverts.  0.23  0.20  0.15  0.16  0.20  0.13  0.10  0.16  0.12  0.10  Epifaunal carn. inverts  0.23  0.20  0.21  0.23  0.20  0.19  0.15  Shrimps and prawns  3.00  0.71  0.64  0.50  0.70  Jellyfish and hydroids  Squid  3.67  0.91  0.89  0.70  0.90  Carn. zooplankton  0.34  0.26  Juv. scraping grazers Detritivore fish Penaeid shrimps  Octopus Ad. large sharks  Prey  Large crabs  Bivalves  Juv. coral trout  Predator  Infaunal inverts.  0.12  0.10  0.13  0.14  0.10  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.23  0.20  0.27  0.29  0.20  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.16  0.20  0.15  0.16  0.20  Large herb. zooplankton  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Mysticetae  0.02  0.02  0.02  0.02  0.02  Small herb. zooplankton  < 1E-2  < 1E-2  < 1E-2  0.01  < 1E-2  Pisc. odontocetae  0.02  0.02  0.02  0.02  0.02  Macro algae  0.16  0.20  0.15  0.16  0.20  Deep. odontocetae  0.02  0.02  0.02  0.02  0.02  Sea grass  0.16  0.20  0.15  0.16  0.20  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  0.01  < 1E-2  < 1E-2  < 1E-2  Birds Reef assoc. turtles  0.11  0.10  0.10  0.11  0.10  Green turtles  0.11  0.10  0.10  0.11  0.10  Fishery discards Detritus Juv. large sharks  Ad. groupers  0.03  0.03  0.03  0.03  0.03  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Oceanic turtles  0.11  0.10  0.10  0.11  0.10  Sub. groupers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Crocodiles  0.11  0.10  0.10  0.11  0.05  Ad. snappers  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. groupers  0.02  0.02  0.02  0.02  0.02  Sub. snappers  0.04  0.04  0.04  0.04  0.04  Sub. groupers  0.09  0.09  0.08  0.09  0.09  Ad. Napoleon wrasse  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Ad. snappers  0.07  0.07  0.06  0.07  0.07  Sub. Napoleon wrasse  < 1E-2  < 1E-2  < 1E-2  < 1E-2  < 1E-2  Sub. snappers  0.30  0.30  0.27  0.29  0.30  Ad. large sharks  0.04  1.00  Ad. Napoleon wrasse  1.00  0.03  0.02  0.02  0.02  Juv. large sharks  Sub. Napoleon wrasse  0.05  0.05  0.05  0