GIS Application for Ecosystem Approach to Fisheries Management in Indonesia
GIS Application for Ecosystem Approach to Fisheries Management (EAFM) in Indonesia
By Gilar Prakoso
A MAJOR PAPER SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENVIRONMENTAL SCIENCE AND MANAGEMENT UNIVERSITY OF RHODE ISLAND MAY 2016 MAJOR PAPER ADVISOR: Dr. David Bengtson MESM TRACK: Environmental Policy and Management
ABSTRACT A GIS application for Ecosystem Approach to Fisheries Management (EAFM) was developed to understand the performance of EAFM in Indonesia. The objects of this application are eleven Indonesian Fisheries Management Areas and Anambas Marine Protected Area. Methods used in this project were divided into three sections: (1) Build the EAFM measurement tool based on EAFM model in Indonesia; (2) Integrate the performance of EAFM into ArcGIS software; and (3) Develop the Indonesia EAFM geodatabase, in order to analyze the performance of EAFM along with other data that are available. The results of this project can help stakeholders to understand the performance of EAFM and provide information for decision making.
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ACKNOWLEDGMENTS
I would like to express the deepest gratitude to my advisor Dr. David Bengtson for all his full support, understanding, and motivation throughout this project. In addition, I express my appreciation to Dr. Peter V. August for his help and kindness as my Environmental Policy and Management Track Chair in MESM program. Also, thank you to Dr. Y.Q. Wang for his patience in teaching me about GIS and Remote Sensing. I would like to thank Dr. Kathleen M. Castro, Barbara Somers and Laura Skrobe for all their assistance during my study at the University of Rhode Island. Thanks also go to my fellow graduate students at MESM. Special thanks go to my Indonesian friends for all the laugh and invaluable experience during our time in the USA. Finally, I would like to thank my wife, kids, and parents for their unconditional love and support during the last two years. I would not have been able to complete my master degree without their endless love and encouragement.
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TABLE OF CONTENTS ABSTRACT..................................................................................................................................... i ACKNOWLEDGMENTS .............................................................................................................. ii LIST OF TABLES ........................................................................................................................ iv LIST OF FIGURES .........................................................................................................................v INTRODUCTION ...........................................................................................................................1 OBJECTIVES ..................................................................................................................................7 METHODOLOGY ..........................................................................................................................7 Study Area .................................................................................................................................7 Data ..........................................................................................................................................10 EAFM Model ......................................................................................................................10 EAFM Indicators Performance..........................................................................................11 Geospatial Data .................................................................................................................12 Data Processing and Analysis ..................................................................................................12 EAFM Measurement Tool ..................................................................................................12 EAFM Integration into ArcGIS 10.3 ..................................................................................14 EAFM Geodatabase ...........................................................................................................16 RESULT AND DISCUSSION ......................................................................................................16 GIS Application for EAFM .....................................................................................................16 Geodatabase Application .........................................................................................................20 CONCLUSION ..............................................................................................................................20 REFERENCES ..............................................................................................................................21 APPENDICES Appendix 1 – EAFM Assessment on Social Indicators ...........................................................23 Appendix 2 – List of Feature Class inside Geodatabase File ..................................................24 Appendix 3 – The use of Geodatabase in Eastern Indonesia ..................................................25
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LIST OF TABLES Table 1. EAFM Indicators in Indonesia ...........................................................................................4 Table 2. Flag Model of EAFM in Indonesia ..................................................................................11 Table 3. EAFM Performance in Anambas MPA ...........................................................................18
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LIST OF FIGURES Figure 1. Map of Indonesia’s Fisheries Management Areas ...........................................................8 Figure 2. Marine Protected Area distribution ..................................................................................9 Figure 3. Location of Marine Protected Area in Anambas Island Region.......................................9 Figure 4. Example of EAFM assessment on fish resource domain ...............................................13 Figure 5. The spreadsheet of cumulative index .............................................................................14 Figure 6. The interface of MyGeoData ..........................................................................................15 Figure 7. The attribute table of FMA shapefile/feature class after modification...........................15 Figure 8. The attribute table of FMA shapefile/feature class with the value of EAFM composite index................................................................................................................................16 Figure 9. The performance of EAFM domains in Indonesia’s Fisheries Management Areas ......17 Figure 10. EAFM performance of Anambas MPA and Jemaja Island land cover ........................19 Figure 11. Example of the use of geodatabase files.......................................................................20
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INTRODUCTION Economic development and population growth have increased the utilization of the coastal area in Indonesia without proper consideration of environmental sustainability. The rapid growth and dense population in the coastal zone has caused major environmental damage such as deforestation of mangrove ecosystems, reduction in the coral reef, seagrass bed and estuary ecosystem, increase in pollution from land activities, and over-fishing as a major problem (MMAF, 2006). Moreover, low awareness of coastal communities on coral reef conservation, inadequate institutional capacity to contain land and marine-based pollution, insufficient institutional frameworks, and persistent poverty in coastal areas have resulted in degradation of about 70% of Indonesia’s coral reef ecosystem (Indonesia CTI, 2012). The Research Centre for Oceanography of the Indonesian Institute of Sciences (2014) revealed that only 5.32% of coral reefs in Indonesia is classified as very good, whereas 27.20% of them were classified in good condition, 37.42% were sufficient, and 30.07% were in bad condition. Burke et al. (2012) mentioned that in the last half century, degradation of coral reefs in Indonesia increased from 10% to 50%. In addition, Indonesia has also lost most of its mangrove. From 1982 to 2000, Indonesia has lost more than half of the mangrove forest, from 4.2 million hectares to 2 million hectares (NASA, 2010). Hughes et al.’s (2012) comparison of Indonesian fisheries with those of 27 other countries, indicated that they are the most vulnerable to collapse based on management indicators of coral reef, fisheries status, and food security. Moreover, some Fisheries Management Areas (FMAs) in Indonesia have faced the symptoms of overfishing for some groups of essential commodities, such as large pelagics, small pelagics, shrimp and demersal fish (MMAF, 2011). Four of the eleven FMAs have exceeded potential production. For example, only three FMAs do not show symptoms of overfished shrimp, whereas for demersal fish, two FMAs are already overfishing and five are categorized as fully exploited. These problems highlights the urgent need for all parties to work together on improving fisheries management and its linkage to the ecosystem. Effective fishery management must include the three equal and interconnected aspects of the natural, human and fishery management systems. Conventional approaches 1
to fisheries management only partially consider these dimensions. The Ecosystem Approach to Fisheries Management (EAFM) introduces a series of modifications to conventional fisheries management to improve performance and contribute to sustainable development. The shift to an EAFM from traditional fisheries management requires thoughtful consideration of some important issues and challenges. One of them is the availability and analysis of integrated geospatial data regarding the identification process, verification, decision-making, and monitoring-evaluation. Accurate assessment of EAFM performance requires reliable data at a range of scales. Enhancement and strengthening of data collection systems will assist EAFM implementation in Indonesia. This is the primary role of GIS application, which is becoming a tool that can combine, analyze and present the results of the integrated mapping for all required data. The problems are how to create a linkage system between EAFM indicators and GIS application, and how the GIS will contribute to fisheries management in Indonesia. Therefore, this project aims to develop GIS application based on the analytical model of an ecosystem approach assessment. Ecosystem Approach to Fisheries Management (EAFM) The UN Food and Agriculture Organization (2003) defines EAFM as “an approach to fisheries management and development that strives to balance diverse societal objectives by taking into account the knowledge and uncertainties about biotic, abiotic, and human components of ecosystems and their interactions and applying an integrated approach to fisheries within ecologically meaningful boundaries.” Referring to that definition, EAFM can be understood as a concept of how to balance socio-economic objectives in fisheries management (welfare of fishermen, judicuous utilization of fish resources, etc) while still considering the knowledge, information and uncertainties about biotic components, abiotic and human interaction in aquatic ecosystems through an integrated and sustainable fisheries management. Ecosystem‐based fishery management is a new direction for fishery management, essentially reversing the order of management priorities so that management starts with the ecosystem rather than a target species (Pikitch et al., 2004). EAFM differs from conventional fisheries management in that, at its core, EAFM seeks to manage fisheries 2
within the context of the ecological and social systems in which they exist. Recognizing the need for an ecosystem approach stems from the increased understanding of fisheries systems holistically: the interactions within and among fish species; the habitat and wider ecosystem; the fish and fishermen; fishing communities; and broader social, economic, and governance systems that support and influence them. EAFM gives a more extensive structure to manage marine assets and promote improvement through enhanced ecological welfare (e.g. territory assurance and rebuilding, contamination reduction and waste management, sustainable fisheries resources) and human prosperity (e.g. food security, sustainable livelihoods, equitably distributed wealth). Zhang et al. (2009) predicted that, when fishery managers understand the complex ecological and socio-economic environments in which fish and fisheries exist, they will be better able to anticipate the effects that fishery management will have on the ecosystem, as well as the effects that ecosystem change will have on fisheries. Moreover, EAFM can be executed crosswise over distinctive spatial and governance scales and can be customized to suit prioritization of real issues and objectives. EAFM requires a comprehensive analysis and decision-making method based on spatial data and metadata information that covers multi-layer aspects like physical, ecological, social and economic. Therefore, the use of GIS has a significant role in achieving that requirement. EAFM Indicators Zhang et al. (2009) indicated that four principles should apply for the integration of ecosystem considerations into decisions: (1) the approach should be evolutionary rather than revolutionary; (2) it should be capable of being applied with available information; (3) it should be precautionary and environmentally sound; and (4) it should be simple and pragmatic. In the context of fisheries management, the desirable properties of indicators are: (1) directional; (2) sensitive to change; (3) range spans natural variability; (4) precision and variance estimable and reasonable; (5) unambiguous; (6) not duplicative nor repetitious; and (6) expressive or representative of key processes (Link, 2010). From these principles and properties, stakeholders need to set indicators and benchmarks to measure management performance to determine whether management is meeting the objectives. 3
“Indicators and benchmarks are developed only after an objective has been agreed upon. An indicator tracks the key outcome identified in the objective and, when compared with an agreed-upon benchmark (often a target or a limit value or trend), provides a measure of how well management is performing (performance measure). A performance measure is simply the difference between the indicator value and its benchmark (often referred to as reference points) at any time of assessment. The group of objectives, indicators, benchmarks, and performance measures provide a means of communication with decision makers and their ability to make appropriate changes in management.” (Staples & Funge, 2009). The main purpose of developing a set of indicators is to assist in assessing the performance of EAFM and to stimulate action for better sustainability objectives. They can also enhance communication, transparency, effectiveness and accountability in fisheries management. In order to implement EAFM in Indonesia, a set of indicators is required to monitor and evaluate progress. A suite of indicators was developed in consultation with the primary stakeholders in fishery management. The results are 33 indicators spanning six fishery domains: fish resource, habitat, fishing technique, social, economic and institutional (Table 1). Table 1. EAFM Indicators in Indonesia Domain Indicator Raw CPUE Fish size trend Proportion Juvenile caught Fish Resource "Range Collapse" Fish catch composition
Habitat
Fishing Technique
Species of ETP (endangered, threatened, and protected species) Quality of waters Status of seagrass Status of mangrove Status of coral reefs ecosystem Unique habitat Climate change on habitat and waters condition Destructive fishing technique Modification of fishing tools and FAD Fishing capacity and effort
Weight 40 20 15 10 10 5 25 20 15 15 15 10 30 25 15
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Domain
Indicator Fishing selectivity
Social
Economic
Institutional
Weight 15
The compliance function and the size of vessel with legal documents Certification of fishing crew Stakeholder participation Fisheries conflict Local knowledge utilization Asset ownership Household fishery Saving ratio
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Pursuance of responsible fisheries principle in formal or non-formal fisheries management plan Completeness rules in fisheries management Decision rules mechanism Fisheries management plan
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Fisheries management policy and synergetic institutional level Stakeholder capacity
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5 40 35 25 45 30 25
26 18 15
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Note. Adapted from Assessment of Ecosystem Approach to Fisheries Management Indicators by MMAF, 2014a
Geographic Information Systems (GIS) Geospatial technology describes the use of a number of different high-tech systems and tools that acquire, analyze, manage, store, or visualize various types of location-based data. GIS is defined as a computer-based mapping, analysis, and retrieval of location-based data (Shellito, 2012). “In short, GIS adds value to spatial data. By allowing data to be organized and viewed efficiently, by integrating them with other data, by analysis and by the creation of new data that can be operated on, GIS creates useful information to help decision-making” (Heywood et al. 2006). From this brief definition, it may be clear that GIS can be divided into a number of essential components: •
“People. GIS cannot operate in isolation from the organizational context, and there must be people to plan, implement and operate the system, as well as to make decisions based on the output.
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Data. For most GIS operatives, data have now become the most important element to GIS, a fact largely based on their high costs relative to other operating costs. Today, a vast array of data is available from varied and diffuse sources. The 5
requisite data for any specific project must be carefully identified and acquired, and the quality of these data will determine the usefulness of the final GIS output. •
Hardware. A range of hardware exists for transforming data into digital formats, which must be stored, manipulated and processed by computers before output can be obtained via plotters, printers and screens.
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Software. GIS has the potential to utilize a range of software for carrying out a variety of tasks, most of which provide the essential instructions and other linkages between the data and the hardware.
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Procedures. Analysis requires well-defined, consistent methods to produce accurate, reproducible results” (FAO, 2013). Meaden (2007) states that GIS has become virtually ubiquitous in the world of
science and research, if not yet in the world of fisheries or its management. It is also worth noting that almost all uses of GIS in the fisheries area, have been related to more-or-less static mapping, e.g. creation of nautical charts, coastal zone management, optimum locations for aquaculture, etc., and it was only in an examination of the potential uses for GIS that mention was made of anything more complex. Nowadays, there is almost no facet of the use of GIS for spatial analys that cannot be attempted. The many benefits of GIS to assist in fisheries management include, habitat mapping, fish distribution, fishing fleet disposition, resource analyses, vessel monitoring system, and marine reserve allocation. Moreover, spatial analysis using GIS is recognized as an essential tool to integrate ecosystem information from various sources. An experienced GIS user can run many analyzes using off-the-shelf software and readily available data sets – but customized tools can streamline the workflow of these analyzes, and make them feasible for someone with less expertise. One successful example of how GIS application contributes to EAFM is the EcoGIS project by NOAA. The EcoGIS project was launched in September 2004 to investigate how GIS, marine data, and custom analysis tools can better enable fisheries scientists and managers to adopt EAFM (NOAA, 2009). Furthermore, the project has focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization, Bycatch Analysis, and Habitat Interactions. The main result is a working prototype for catch and effort analysis, the Fishery Mapper Tool.
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Finally, GIS plays a central role in information management, including natural resource management like fisheries. A growing problem confronting natural resource managers is the management of several interdependent resources, each of which has multiple uses and multiple users with multiple value systems (McCormick 1999). The challenges of natural resource management are to improve our understanding of complex ecosystem dynamics, better assess natural resources and protect or restore natural systems. Thus, GIS can directly benefit the assessment of natural resources, which is to visualize a complex ecosystem at different scales, as well as spatially distributed environmental impacts. OBJECTIVES The objectives of this project are to: 1. Develop a simple tool in Microsoft Excel to the EAFM model in Indonesia. 2. Develop a GIS application based on an analytical model of EAFM. 3. Develop a geodatabase file to support EAFM in Indonesia. Project location is Indonesia, and the primary client is Ministry of Marine Affairs and Fisheries (MMAF), Republic of Indonesia. The GIS application will cover the scope of Indonesia Fisheries Management Areas (FMAs) or Marine Protected Areas (MPAs). METHODOLOGY Study Area GIS development in this paper is based on fishery region. The objects used to test the application are Indonesian FMAs and one MPA location in Indonesia. Fisheries Management Areas of the Republic of Indonesia are management areas for fishing, fish farming, conservation, research, and development of fisheries which include internal waters, archipelagic waters, territorial sea, contiguous zone and the Exclusive Economic Zone of Indonesia (MMAF, 2011). The Indonesian marine waters are divided into 11 (eleven) FMAs, which are (Fig. 1): FMA-RI 571 (Malacca Strait and Andaman Sea), FMARI 572 (Indian Ocean of Western Sumatera and Sunda Strait), FMA-RI 573 (Indian Ocean of Southern Java, Southern Nusa Tenggara, Sawu Sea, and Western Timor Sea), FMA-RI 711 (Karimata Strait, Natuna Sea and South China Sea), FMA-RI 712 (Java Sea), FMA7
RI 713 (Makassar Sea, Bone Bay, Flores Sea and Bali Sea), FMA-RI 714 (Tolo Bay and Banda Sea), FMA-RI 715 (Tomini Bay, Maluku Sea, Halmahera Sea, Seram Sea and Berau Bay), FMA-RI 716 (Sulawesi Sea and Northern of Halmahera Island), FMA-RI 717 (Cendrawasih Bay and Pacific Ocean), and FMA-RI 718 (Aru Bay, Arafura Sea and Eastern Timor Sea).
Figure 1. Map of Indonesia’s Fisheries Management Areas (Source: MMAF, 2014b)
Marine Protected Area is “a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values” (IUCN, 2008). MMAF (2014c) defined MPA as a protected area, which is managed on zoning system, to realize the sustainable fisheries resources and environment management. Based on data from Directorate of Conservation of Areas and Fish Species, MMAF, in May 2016, there were 17,302,747.78 hectares of MPAs Indonesia-wide. These comprise 154 MPAs, of which 32 managed by Ministry of Forestry (MoF) and 122 by local governments and the MMAF.
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Figure 2. Marine Protected Area Distribution (MMAF 2016, adapted from www.kkji.kp3k.kkp.go.id)
For the purpose of this project, demonstration of GIS application for EAFM focuses on Anambas Islands, Riau Islands Province. Anambas Islands is a small archipelago of Indonesia, located 150 nautical miles northeast of Batam Island in the South China Sea between the Malaysian mainland to the west and the island of Borneo to the east. Marine Protected Area of Anambas is a conservation area under MMAF and local governance with potential of ecotourism and abundant fish resources. The total area of Anambas MPA is 1,262,686.2 hectares (MMAF, 2014c).
Figure 3. Location of Marine Protected Area in Anambas Island Region
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Data Data used in this project are categorized as secondary data. The data includes EAFM models, EAFM performance indicators, and geospatial data. EAFM Model An EAFM model was used as a primary reference in building the logic of the application of GIS. The EAFM Model that was applied by the Government of Indonesia was created using a Multi-Criteria Analysis (MCA) approach. MCA is a decision-making tool developed for complex multi-criteria problems that include qualitative and/or quantitative aspects of the problem in the decision-making process (Prabhu et al., 1999). Moreover, “MCA techniques can be used to identify a single most preferred option, to rank options, to short-list a limited number of options for subsequent detailed appraisal, or simply to distinguish acceptable from unacceptable possibilities” (DCLG, 2009). Technical flag modeling was done by a MCA in which a set of criteria was built as a base for the performance analysis of the fishery management area seen from the ecosystem approach through the development of a composite index, with the following stages (Adrianto et al., 2005; MMAF, 2011; Pregiwati et al., 2015): 1. Determine the criteria for each indicator in each domain of EAFM (fish resource, habitat, fishing technique, social, economic and institutional); 2. Assess the performance of each object (based on location or species) for each indicator tested; 3. Give a score for each of the performance indicators in each object (Likert score based ordinal 1,2,3); 4. Determine the weighting for each indicator; 5. Develop a composite index of each domain for each object with a model function: CAi = f (CAni….n=1,2,3…..m) 6. Develop a composite index for the entire EAFM on each object with the model functions as follows: C-object-i = f (CAiy 1,2,3...... y = 1,2,3….. z; z = number of object).
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Indicators are assessed and then analyzed using a simple composite based on the arithmetic average to display in the form of the flag model (MMAF, 2011) as shown in the following table. Table 2. Flag Model of EAFM in Indonesia Score Value Flag Model 100-125 126-150 151-200 201-250 251-300
Description Bad Not good Medium Good Best
EAFM Indicators Performance To test the GIS application, this project used the results of EAFM indicators assessment that was done previously. Two EAFM performance data sets were employed in this project: (1) EAFM assessment of 11 FMAs in Indonesia carried out by MMAF in 2011; and (2) EAFM assessment of Anambas Marine Protected Area conducted by Pregiwati et al. in 2015. The assessment of 11 FMAs in Indonesia is the result of collaboration between MMAF, Indonesia World Wildlife Fund (WWF) and the Center for Coastal and Marine Resource Studies (CCMRS) - Bogor Agricultural University. This study is done by using the content analysis approach, in which the study focused on the substance of the performance of fisheries management in FMAs and then was tested with EAFM indicators. Moreover, this study has a limitation regarding data collection because it is based solely on secondary data and data that were sourced on the administrative scale at the provincial level. Therefore, caution is needed to interpret the results of the assessment. EAFM assessment of Anambas Marine Protected Area is part of a study titled “Linking Indicators for Ecosystem Approach to Fisheries Management and Management of Marine Protected Areas Effectiveness in Anambas Island, Indonesia”. According to Pregiwati et al. (2015), the research was conducted in March 2015 to June 2015, and data collected in this study consisted of primary and secondary data. The primary data were obtained through direct observation and in-depth interview.
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Geospatial Data Shellito (2012) defined geospatial data as information that refers to location-based data, which is tied to a specific real-world location. The ability to assign a location to data is what makes geospatial technology different from other systems. The main benefit of using geospatial technology is to link non-spatial data to a location. In this project, all data related to EAFM model and EAFM performance are categorized as non-spatial data. Meanwhile, geospatial data collected for this project are: 1. Indonesia’s administrative boundary (shapefile); 2. Indonesia’s Fisheries Management Areas (KMZ file); 3. Bathymetry (shapefile); 4. Ocean topography (raster dataset); 5. Coral reefs and mangroves (shapefile); 6. Lakes, reservoirs, rivers and roads (shapefile); 7. Fishing port and fish landing sites (shapefile); 8. Marine Protected Areas (KMZ file) The geospatial data above are combined with EAFM indicators performance data and other non-spatial data inside a geodatabase file in ArcGIS 10.3, such as provincial fisheries statistics and national fisheries statistics. The geodatabase is a collection of files in a folder on disk that can store, query, and manage both spatial and non-spatial data. Furthermore, the geodatabase is the core geographic information model to organize GIS data into thematic layers and spatial representations (ESRI, 2016). Data Processing and Analysis In accordance with the objectives of this project, data processing was divided into three sections: (1) Build the EAFM measurement tool with Microsoft Excel; (2) Integrate the performance of EAFM into ArcGIS software; and (3) Develop the Indonesia EAFM geodatabase, in order to demonstrate the performance of EAFM along with other data that available. EAFM Measurement Tool Implementation of EAFM needs to be supported by a simple tool to measure its performance in a particular fisheries management unit. This tool was developed in the 12
Excel file as an assessment template, which is based on the EAFM model and its indicators. The indicators are compiled separately by their respective fisheries domain. Moreover, each domain is created in a different spreadsheet with eight main columns in each sheet: indicators, definition, data collection/monitoring, criteria, reference data, score, weight, and value. Performance value is at the bottom of the“value” column, which indicates the overall performance of a domain. The automated flag color was constructs by the Conditional Formatting tool in Excel with format style: 3-color scale (minimum-1-red, midpoint-2-yellow, and maximum-3-green).
Figure 4. Example of EAFM assessment on fish resource domain
A cumulative index and histogram bar are displayed in the spreadsheet to help the user interpret the evaluation result. The cumulative index is a compiled index that reflect the effect of all accumulated data. The value of the cumulative index is generated automatically based on the input value in the sheet of each domain. From this spreadsheet, the user can decide which domain or indicator needs more attention for management improvement.
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Figure 5. The spreadsheet of cumulative index
EAFM Integration into ArcGIS 10.3 The cumulative index from the EAFM assessment was added as a new field in the shapefile attribute table. Attribute tables are often joined or related to spatial data layers, and the attribute values they contain can be used to find, query, and symbolize features or raster cells (ESRI, 2016). Shapefiles used as reference are geospatial data that represents the study objects, which are FMAs and Anambas MPA. Spatial data of FMAs and Anambas MPA were sourced from MMAF (downloaded from http://sig.kkp.go.id/). Data were obtained in the form of a KMZ file (Keyhole Markup-language Zipped), which is a file extension for a placemark file used by Google Earth. Furthermore, the file was converted into a shapefile, so that it can be displayed in ArcGIS 10.3. In this project, the conversion process used the online converter named MyGeoData (http://converter.mygeodata.eu/). This Converter has a user-friendly interface and fast processing, the conversion process was done by just uploading the KMZ file into the website.
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Figure 6. The interface of MyGeoData (source: http://converter.mygeodata.eu/)
The FMAs shapefile consists of eleven polygon areas that represent their location in Indonesian water. The geographic coordinate system of this file is GCS_WGS_1984. Then, this file was projected inside a data layer into Asia South Albers Equal Area projection for calculation purposes. The original data consisted of fields in Indonesian language, so editing was applied to translate all the fields and their values. After translation, the original fields include Objectid, Shape, FMA, and Archeology_Zone. Furthermore, the EAFM domains were added to the table, along with the “Area” field by the Calculate Geometry command. The FMA attribute table serves as the main connector between the result of EAFM assessments in Excel and FMAs spatial data in ArcGIS. The result of modification of the FMA attribute table can be seen in the figure below.
Figure 7. The attribute table of FMA shapefile/feature class after modification
Almost all the steps above were also applied to build Anambas MPA. The only difference is the step to extract the Anambas MPA from the original file that covers all of the MPAs in Indonesia; this process was applied by the Export Selected Features command
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in ArcGIS. The fields for Anambas file after modification consist of Objectid, Shape, Name, Area_ha, and then followed by six EAFM domains. EAFM Geodatabase Compilation data inside Geodatabase on this project were derived from government agencies, which are: Centre for Analysis on International Cooperation and Inter Institution – MMAF, Directorate of Fish and Area Conservation – MMAF, Geospatial Information Agency, and Research Centre for Oceanography – The Indonesian Institute of Sciences. In addition, new data were developed from the book “Statistics of Marine Capture Fisheries by Fisheries Management Area (FMA), 2005 - 2014”, published by Directorate General of Capture Fisheries – MMAF in 2016. These data provided as additional information to analyze the performance of EAFM. Furthermore, all the data were converted into feature classes in the geodatabase using ArcCatalog application. RESULT AND DISCUSSION GIS Application for EAFM The application built using Excel and ArcGIS was combined with the data of EAFM indicators assessment, both for eleven Fishery Management Area and also for Anambas MPA. The composite index of each domain was inserted into the attribute table. For easier interpretation, quantities symbology was applied with a manual classification that refers to the flag model of EAFM in Indonesia. The FMAs attribute table with the value of EAFM composite index can be seen in the following figure.
Figure 8. The attribute table of FMA shapefile/feature class with the value of EAFM composite index
From the figure above, it can be seen that the lowest domain is the economy, with an average composite index for all FMAs of 163.64. The indicators of the economy domain 16
are asset ownership, household fishery and saving ratio. This condition suggests that the availability of fish resources with the existing management have not been able to guarantee the welfare of the fishermen in Indonesia, especially for artisanal fisheries. Further interpretation can be done by looking at the following GIS visualization with ArcGIS.
Figure 9. The performance of EAFM domains in Indonesia’s Fisheries Management Areas
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The biggest gap between the economy index and the resources aspects (habitat and fish resources) occurs in FMA 711 (Karimata Strait, Natuna Sea, and South China Sea). From Figure 9, the economy index of FMA 711 is considered bad, even though its performance on habitat and fish resources are classified as best and good respectively. Why fishermen in this area remain relatively poor, should be studied in the context of IUU fishing activity by other countries in this area, and/or the lack of infrastructure and technology. Many things can be questioned and analyzed. The EAFM performance displayed via GIS application allows the user to access the information faster, to more focus on the priority issues for deeper evaluation, and then respond with effective action. The use of GIS was also demonstrated for the Anambas MPA. According to Pregiwati et al. (2015), domains of fish resources, fishing technique and economy are in the good category with the green flag, while domains of social, habitat and institutional are in the medium category with the yellow flag, so the total average assessment is in the good category. The composite value of each EAFM domain can be seen in the following table. Table 3. EAFM Performance in Anambas MPA Domain Value Flag Model Fish resource 209 Habitat 191 Fishing technique 269 Social 185 Economy 205 Institutional 186 Overall 207.5
Description Good Medium Best Medium Good Medium Good
From Table 3, the lowest value of EAFM domain is Social with the score 185. The social domain contains three indicators, which are stakeholder participation, fisheries conflict and local knowledge utilization. The question is which indicator contributes most strongly to the adverse impact in the social domain. The answer will be displayed in the GIS visualization of the Anambas MPA. Moreover, the EAFM performance is presented along with additional information on one of the island in Anambas (Fig. 10). The island selected for this display is Jemaja Island, which has a long stretch of beach and is surrounded by abundant coral reef ecosystems. This island is one of the most beautiful islands in Indonesia for ecological tourism. 18
Figure 10. EAFM performance of Anambas MPA and Jemaja Island land cover
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Geodatabase Application Data from several agencies were compiled as feature classes inside the geodatabase file. The file geodatabase uses an efficient data structure that is optimized for performance and storage. According to ESRI (2016), the data structure of the geodatabase file uses about one-third the feature geometry storage required by shapefiles. The FMAs and MPAs shapefiles from this project are combined with other spatial/non-spatial data for management purpose. This geodatabase is a dynamic file, which is editable and can be updated continually.
Figure 11. Example of the use of geodatabase file
From this geodatabase file, a user can display the map on national scale to show all locations of fishing ports, fish landing sites and MPAs in Indonesia, like the example in Figure 11. Moreover, the user can also focus on a particular regency, province, or FMA. Non-spatial data can be linked in this geodatabase to gain more additional information, such as fisheries statistics, population density, land use/cover, and much more. The list of feature classes compiled in this geodatabase file can be seen in Appendix 2. CONCLUSION The use of this GIS application can help stakeholders to understand the performance of EAFM in Indonesia. The result of EAFM measurement can be visualized through GIS application, and successfully provide information for decision making. The next challenge is to develop this project into an online basis, so it can be accessed by anyone anywhere. 20
REFERENCES Adrianto L., Matsuda Y., Sakuma Y., 2005 Assessing sustainability of fishery systems in a small island region: flag modeling approach. Proceeding of IIFET 2005, Tokyo, pp. Burke et al. 2012. Reefs at risk, Revisited in the Coral Triangle. World Resources Institute. DCLG (Department for Communities and Local Government). 2009. Multi-criteria analysis: a manual. London. ESRI. 2016. FAO. 2003. Fisheries Management. The Ecosystem Approach to Fisheries. Rome. FAO FAO. 2013. Advances in Geographic Information Systems and Remote Sensing for Fisheries and Aquaculture. Summary Version. . Heywood, I., Cornelius, S. & Carver. S. 2006. An introduction to geographical information system (3rd ed). Harlow, UK, Pearson Education Ltd. Hughes, S., A. Yau, L. Max (more), 2012: A framework to assess national level vulnerability from the perspective of food security: The case of coral reef fisheries. Environmental Science and Policy, 23, 95-108, DOI: 10.1016/j.envsci.2012.07.012. IUCN/WCPA. 2008. Guidelines for applying protected area management categories. 3rd draft of revised guidelines. Indonesia National Coordinating Committee Coral Triangle Initiative on Coral Reefs, Fisheries and Food Security. 2012. The State of the Coral Triangle in Indonesia. Jakarta. Link, Jason S. 2010. Ecosystem-based fisheries management: confronting tradeoffs. UK. Cambridge University Press. McCormick, F.J. 1999. Principles of ecosystem management and sustainable development. In Peine, J.D. (ed.). Ecosystem management for sustainability: principles and practices illustrated by a regional biosphere reserve cooperative, pp. 3– 21. Lewis Publishers, Boca Raton, FL. Meaden, G. 2007. Geographical Information Systems (GIS) in Fisheries Management and Research. n. p. doi 10.1007_978-1-4020-8636-6_4. Ministry of Marine Affair and Fisheries (MMAF) – Republic of Indonesia. 2006. Model Content Materials for Drafting Regional Regulations for the Provinces and Districts Cities on Coastal Area Management. . MMAF [Directorate General of Capture Fisheries - Ministry of Marine Affairs and Fisheries], [WWF-Indonesia] World Wide Foundation, [CCMRS-IPB] Center for Coastal and Marine Resources Studies, Bogor Agricultural University, 2011. [Ecosystem Approach to Fisheries Management in Indonesia – Early Study Performance Ecosystem Approach to Fisheries Management in Indonesia Regional Fisheries Management]. Jakarta. [In Indonesia]. MMAF [Ministry of Marine Affairs and Fisheries] – Republic of Indonesia. 2011. Keputusan Menteri Kelautan dan Perikanan Republik Indonesia Nomor 45/Men/2011 21
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Appendix 1. EAFM Assessment on Social Indicators
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Appendix 2. List of Feature Class inside Geodatabase File Feature Class SANDSHL ROADWL NATROADWL LUNITA LANDUA OneMap_Reef NEIGHBA LU_Natuna LAMACAM HYDROLPDTK HYDROAKLH HARBFISHLN HARBFISH FMAs FISHSTATFMA FISHGR FISHSTATPROV FISHCATCH MINIAP CSSEA CSPARK CSNATURE Coral_Jemaja BSDSTA Anambas_MPA Adm_ID Adm_AN BATHYA DEM_Laut
Explanation Estuaries National Road Roads Seabed Surface Layer Sediment Land Use/Cover Coral Reef Neighbour Countries Land Use Natuna Islands Mangrove River Lake/Reservoirs Fish Landing Sites Fishing Port FMAs with EAFM Performance FMAs Fisheries Status Fishing Ground Location Province Fisheries Statistics FMAs Fisheries Statistics MPA 1 MPA 2 MPA 3 MPA 4 Coral Reef Jemaja Island Villages Database MPA of Anambas Islands Indonesia Administrative Boundary Anambas Islands Bathymetry Ocean Digital Elevation Model
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Appendix 3. The Use of Geodatabase in Eastern Indonesia
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