Part 4: Case Studies

We spoke with several experts to help identify promising initiatives in addition to conducting online research. From an original list of seventeen candidates, we selected eight case studies to pursue in detail. For each, we conducted interviews and background research to surface drivers, enabling conditions, stuck points, and strategies for success.

Table 3. Summary of interviews completed across various stakeholder categories for each Case Study

Atlantic Coastal Cooperative Statistics Program (ACCSP)

Background: The Atlantic Coastal Cooperative Statistical Program (ACCSP) is one of five regional Fishery Information Networks (FINs) in the United States tasked with collecting, managing, and sharing fisheries data. ACCSP was created in 1995 as a multi-stakeholder collaboration to create consistency among state and federal fisheries data systems in order to meet the data needs of managers, scientists, and fishermen, and enhance fisheries management along the Atlantic coast. Program partners include representatives from 23 state, regional, and federal agencies. The creation of the ACCSP was motivated in large part by the need for reliable fisheries data in order to comply with federal regulations. The Magnuson Fishery Conservation and Management Act of 1976 created regional councils, mandated a national fishery data management program, and tasked the councils with using data to establish fishery management plans for fisheries within the EEZ or federal waters. For “interjurisdictional” fishery resources along the Atlantic Coast, interstate fishery management plans were created by the Atlantic States Marine Fisheries Commission (established by Congress as a nongovernmental entity).
Nearly two decades later, in 1993, the Atlantic Coastal Fisheries Cooperative Management Act enabled the states to “mutually and cooperatively” manage those interjurisdictional fisheries under interstate fishery management plans. Mutual and cooperative management requires data integration among partnering states, which didn’t exist at that point. Thus, ACCSP was created to establish standardized codes, formats, and protocols that would enable coordinated collection, reporting, integration, and analysis of accurate fishery-dependent data in order to meet the data needs of managers, scientists, regulators, and other land-use and water-use decision-makers. Using a committee-based approach, ACCSP works specifically to (a) standardize data collection activities among partners, (b) streamline electronic trip and dealer reporting and data processing using mobile and web applications via SAFIS (the Standard Atlantic Fisheries Information System), (c) integrate all fisheries-dependent data into a single, unified online database (the Data Warehouse) and make it accessible to stakeholders through user-friendly interfaces, and (d) provide funding support for innovation in data collection and data management activities among partner data projects.
System Attributes:

• Primary Drivers: National Policy; Cross-jurisdiction needs (national)
• Enabling Conditions: Existing Data Sharing Protocols and Initiatives; Government Assumes Responsibility
• Funding: Dedicated; Internal
• Leadership: Patient Execution; Task-Master
• Timeframe: Ongoing (Initial stages 1-3 took over 20 years)

Stakeholder Engagement & Technology Deployment: ACCSP’s Standard Atlantic Fisheries Information System (SAFIS) was launched in 2003 as a low-cost, real-time, web-based data entry system for dealer reporting of commercial landings. Since then, it has evolved into a comprehensive module-based data collection tool featuring a number of web-based and mobile applications for both commercial and recreational fishing activities:

• eTrips/Online collects catch and effort data from commercial harvesters using a web browser
• eTrips/Mobile is intended for use by commercial harvesters at-sea with a tablet application, regardless of whether an internet connection is available
• eDR/Online and eDR/Mobile electronically collect landings and economic data from dealers using a web browser and mobile application (no internet required), respectively. eDR/Mobile allows for harvester swipe cards, which pre-populate data fields unique to a particular harvester. eDR allows the price of a species to be saved for 14 days and enables dealers to view the history of landings through an interactive report feature
• eLogbook is a voluntary reporting application that collects catch and effort data from recreational anglers.

Chile

Background: Chile’s Fisheries Law of 2013 required all industrial vessels to implement electronic monitoring (EM) by the end of 2018, simultaneously reducing fishing quotas across Chile’s fisheries. After the law was established, illegal fishing began to increase precipitously as fishermen struggled to adjust to the new quota allocations. While artisanal fishers want a legal fishery, reduced quotas are perceived as a challenge to their livelihoods, coupled with a lack of enforcement that makes it difficult for compliant fishers to compete, especially when tonnes of illegal fish are flowing through supply chains with minimal oversight.
In late 2018, Chile passed a SERNAPESCA (Ministry of Economics, Development, and Tourism) modernization law as a supplement to the initial Fisheries Law, which provides SERNAPESCA with additional resources (and responsibility) to combat IUU, particularly in regard to overfished or exploited fisheries. This policy, combined with the need to implement electronic monitoring in the industrial fleet, has driven interest in more holistic fisheries data modernization efforts in order to accommodate increased data flows and deal with the inefficiencies and inaccuracies that currently limit enforcement and management activities.
System Attributes:

• Primary Drivers: Market Differentiations; Sanctions/Trade Barriers (SIMP and EU IUU), On-the Ground NGO Support
• Enabling Conditions: Government Assumes Responsibility; Minimum Viable Participant Group
• Funding: Dedicated; Internal
• Leadership: Amplified Voice; Patient Execution
• Timeframe: 4 years (Stage 1), 1 year + ongoing (Stage 2) = 5+ years

Stakeholder Engagement & Technology Deployment: A select EM team within SERNAPESCA has been working with vendors to identify, select and pilot cameras on boats with select industrial fishers over the last two years. Part of this effort has included extensive research into the technical requirements that the government would need to meet in order to collect, retain, and regularly access as much scientific as well as compliance data as possible.

Ghana

Background: In 2015, in response to an EU yellow card in 2013, Ghana was eager to participate in an EM pilot program run by FAO and GEF’s Common Oceans Areas Beyond National Jurisdiction (ABNJ) project. The goal was to have transparency in their tuna purse seine fleet via EMS, and prove they were catching tuna in compliance with national and international regulations. Working with World Wildlife Foundation (WWF) and the International Seafood Sustainability Foundation (ISSF), the Ghana Fisheries Commission implemented electronic monitoring (cameras, VMS, and Sat-Modem) on 14 boats, monitoring location, fishing activity, and catch-related data. A costs-benefits analysis has been conducted to help build the argument for expansion of this system in Ghana’s fleets. Ghana is also piloting data collection via tablets at a few select landing sites.
System Attributes:

• Primary Driver: Sanctions/ Trade Barrier (EU yellow card);
• Enabling Conditions: Minimum Viable Participants; Existing Willingness and Comfort with Tech; Recognition of Benefits Informs Strategy
• Funding: International Aid
• Leadership: Patient Execution
• Timeframe: 2 years (Stage 1), 3 years (Stage 2) = 5 years

Stakeholder Engagement & Technology Deployment: Interest in a pilot existed within both government and industry from the start as the EU yellow card threatened major international trade (tuna). Government officials worked with one company that was already using EM on six vessels to help convince other vessel owners of the benefits of technology. Significant effort went into easing industry concerns about how data would be used: created MOUs with industry that during pilot, no information would be used for prosecution, only for learnings; industry also could request copies of all their data. One strategy was to use the analogy of CCTV as “everyone” understands the purpose of that technology is surveillance and the data is protected.
Technology was chosen by FAO and the ABNJ team and used DOS, a Spanish company. A one-week in-country training was done by DOS for industry. The NGO ISSF supported training of land-based observers. Upper management focused on retaining institutional memory by handpicking some people within the fisheries commission itself, seven part time (Ghanean government employees) and two permanent staff (biology and statistics). This group of individuals now holds the training and analysis skill set and can pass that on; this approach reduced the risk of hiring someone from the outside who would take all the training and learning with them when the contract ended.

INDONESIA

Background: A number of initiatives have and are being implemented in Indonesia to improve tuna fisheries data and data systems, from the large-scale longline and purse-seine fisheries to the medium to small-scale handline and pole-and-line fisheries. This case study focused on improvements implemented in the handline yellowfin tuna fishery since 2012. This fishery consists of small (<5 gross tons) canoe-like vessels and usually manned by 1 to 2 fishermen. Fishing trips are one-day long and fishermen fish on both free-school and fishing aggregating devices (FADs). Catch is usually landed on beaches in remote locations across Indonesia’s 17,500 islands. The small-scale, low impact, and remote nature of the handline tuna fishery means that it has not been a priority for government data collection systems. However, the handline tuna fishery is currently estimated to contribute at least 16% of the Indonesia catch (and likely more than that given the data gaps). Moreover, in order for Indonesia to be compliant with RFMO data requirements, it must improve its data on small-scale fisheries which may be small in terms of how they operate but catching a significant amount to the total catch.
System Attributes:

• Primary drivers: On-the-ground NGO support (MDPI); NGO or Industry Proof of Concept; Market Differentiations), Cross-jurisdiction needs (national), Trade Barrier (SIMP)
• Enabling conditions: Stakeholders Set and Maintain Commitments, Communication and Feedback Loops; Expertise, at least “borrowed”
• Funding type: Dedicated, Internal, International Aid
• Leadership: Amplified Voice; Patient Execution; Task-Master
• Timeframe: 4 years (stage 1) 3 years (Stage 2), 3 years (Stage 3), 2 years + (Stage 4) = 12 years +

Stakeholder Engagement & Technology Deployment: Several programs have been put in place since 2012 to improve the data for small-scale tuna fisheries including.
USAID IMACS (2012-2014) developed the I-fish database, an online database created to collect fisheries data in small-scale fisheries (Snapper/Grouper and Yellowfin tuna). Anova and MDPI supported its development by setting up data collection programs in tuna landing sites in Indonesia.

• MDPI (since 2013) is a grass root Indonesian NGO which runs data collection and traceability programs funded through various projects and partnerships.
• The IFITT project (2013-2016) and the NWO project (“Technology innovations towards sustainability in Indonesia’s tuna supply chains” (2014-2016) were supported by Wageningen University and implemented by MDPI in the handline and pole-and-line tuna fisheries. The project supported the expansion of MDPI’s data collection program, the development of the I-fish database and the development, piloting, and integration of electronic data capture and traceability technologies including Spot Trace (VMS/GPS device for small-scale vessels, DOCK app (a digital port samping app), OurFish app (a broker/middleman app to record fish transactions), Tally-O (a traceability system for small-scale Indonesian tuna processors) and ThisFish (a consumer facing platform displaying fish traceability information).
• Since 2014, the government of Indonesia launched several fisheries data modernisation initiatives including Satu Data, designed to centralize data systems for all sectors of the economy, including fisheries; STELINA, a government led traceability system for seafood export product; and e-logbooks for vessels >10GT (not applicable for small-scale vessels which are mostly <5GT).
• The Oceans and Fisheries Partnership (USAID Oceans) (2015-2020) took many of the lessons learned from the previous projects and develops, pilots and integrates new and improved data capture and traceability technologies including: PointTrek (VMS system with e-logbook optional feature), Trafiz (improved version of OurFish) and Trace Tales (improved version of Tally-O). USAID Oceans also works to integrate those technologies with existing and developing government data systems (e.g. STELINA, e-logbooks, SatuData).
• In addition, the WCPFC supported Indonesia data improvement efforts since 2010 through the West Pacific East Asia Oceanic Fisheries Management Project (WPEA-OFM).

Madagascar

Background: Madagascar underwent a three-year process to win the bid to serve as host for the Regional Maritime Information Fusion Center (MIFC), a data center that will serve to collect and disseminate information related to maritime crimes, blue economy, and marine environmental protection. At the same time as launching the regional center, the national government launched a parallel National MIFC to coordinate information sharing across Madagascar’s own maritime divisions. Both centers were modeled off Singapore’s Maritime Information Fusion Center (MIFC) and have brought significant technical and analytical capacity to Madagascar’s maritime management. While application to improved fisheries is still nascent, engagement with fisheries division officials and discussions for expansion into that sectors are underway. Importantly, the focus of the National MIFC is on “maritime intelligence” and the center is housed directly under the Prime Minister’s office and not within any specific cabinet; this was done to reinforce the idea for information to be collected and shared cross-departments to support better intelligence and analyses to benefit multiple divisions.
In addition, multiple NGOs have been active in Madagascar in support of dozens of Locally Managed Marine Areas (LMMA), including bringing data collection and analysis capacity. Several initiatives led by the NGOs involve digital data collection and now, a newly launched MIHARI national database promises to help collect, store, and share information about these projects and potentially, data generated by the various NGO and communities.
System Attributes:

• Primary Drivers: Cross-Jurisdiction Needs (regional MIFC), On-the-ground NGO Support
• Enabling Conditions:
  • RMIFC: Existing Willingness and Comfort with Tech (RMIFC); Government Assumes Responsibility; Appropriate Tech Identified Through Pilot (this case, an existing model)
  • NGO Initiatives: Recognition of Broader Benefits Informs Strategy; Communication and Feedback Loops; Recognition of Problem Rooted in Data Gaps
  • Leadership: Amplified Voice; Task Master
• Funding: Dedicated
• Timeframe:
  • RMIFC: Initiate (3 years); Pilot into Establish (3 years)
  • NGO Initiatives: Initiate (1-2 years)

Stakeholder Engagement & Technology Deployment:
Regional MIFC and National MIFC: Madagascar’s government approved resources to send officials to Singapore to study their IFC system and bring back information to inform the development of a system that would be appropriate for the West African context. This investment resulted in a technologically strong proposal that won the bid on an EU RFP to host the Regional MIFC. Continued investment in sending representatives and demonstrating commitment of resources to the project helped them convince other countries during regional meetings that covered a three-year negotiation process. For the regional center, all data is collected in one main server (hub) and countries can access and take back analyses derived from this metadata.
For the National MIFC, placement of the center directly under the Prime Minister’s office has helped to reinforce that this center is focused on intelligence—not on serving any one department but is an information sharing and analysis platform to serve the needs of multiple divisions. This governance structure has helped engage officials across divisions as no one department is benefiting from the additional resources the center provides.
NGO Initiatives: NGOs have focused on giving back to community and ministries valuable information as quickly as possible. Some of the technology in use includes Tableau software for visualizations, and Open Data Kit for standardizing and collecting fisheries information. Training fishers to be able to use technology requires extensive in-person time and they have made sure to support these efforts. Workshops have also been used to bring together NGOs, technologists, and government to identify the key data gaps, set up MOUs and identify ways to move forward as a collective. Database is now built and NGOs are starting to populate it with their information about their projects. This is less sensitive information than the fisheries data itself, which hopefully will be hosted in the database in phase 2 (1-2 yrs from now).

PacFIN

Background: The Pacific Fisheries Information Network (PacFIN) is a state-federal collaboration to collect and manage accurate data for the purpose of effective fisheries management. PacFIN is the oldest of the five Fishery Information Networks in the US and is a program of the Pacific States Marine Fisheries Commission (PSMFC, also known as Pac States). PSMFC is an Interstate Compact formed in 1947 among California, Oregon, Washington, Idaho, and Alaska to collectively support the protection and management of Pacific Ocean fishery resources. PacFIN has been operational since 1981 and is responsible for data standards, data collection, data integration and storage, data quality assurance and control (QA/QC), data processing, and disseminating data to stakeholders and the public through user-friendly web-interfaces.
PSMFC is a unique type of entity, in that it is composed of government representatives and serves fisheries management efforts, but has neither regulatory nor management authority. Thus, as a neutral party, PSMFC tends to be trusted by industry, regulators, and scientists alike. PacFIN, with its focus on receiving and disseminating timely, accurate data is also perceived to be neutral and unbiased, which is one of the keys to its success.
System Attributes:

• Primary Drivers: Cross-Jurisdiction Needs (national); National Policy
• Enabling Conditions: Government Assumes Responsibility; Existing Willingness and Comfort with Tech; Existing Data Sharing Protocols and Relationships
• Funding Type: Dedicated; Internal
• Leadership: Patient Execution; Task-Master
• Timeframe: The timeline for phases 1-4 of this initiatives spanned decades and is ongoing

Stakeholder Engagement & Technology Deployment: Four main objectives guided the design of the PacFIN program and database, and consensus was always the goal in terms of engaging stakeholders and ensuring that all voices were heard. These four objectives included:

• Implementing and managing a Pacific Fisheries Information Network (PacFIN) to aggregate detailed and summarized state and federal fisheries data;
• Providing data-management consultation and technical advice to the Council’s Management Teams and participating agencies upon request;
• Establishing priorities and coordinating plans to improve the efficiency and timeliness of data acquisition and delivery with a minimum of unnecessary duplication; and
• Promoting the development and implementation of coastwide data-collection standards to facilitate aggregation of fisheries data within the PacFIN system.

Retrospective analyses of the early years of PacFIN have identified key decisions that contributed to its overall effectiveness and success. First, early on PacFIN created a set of standard codes for species, area fished, gear, port, and so on that could be applied to every data source across all regions and all time periods. However, as opposed to requiring that the data sources convert their coding schemes to the PacFIN codes, PacFIN did (and still does) the work of translating those source data codes into its own standard codes. Second, the PacFIN database was built to easily create summary reports based on any variable or combination of variables that could be of interest to data analysts. Relatively quickly, PacFIN’s agency-specific reports (e.g., state reports) became preferred over the source-agencies’ own generated reports. Finally, the database was designed to be easily modified and expanded as new needs arose. In fact, most of the enhancements made to the system in the first few years were recommended by users. For example, the Quota Species Monitoring module was created because industry requested more timely data on total catch relative to quotas in order to plan production and marketing.

Peru

Background: Enormous variance among fisheries data collection and sharing systems exist within Peru, from sophisticated systems in the industrial anchoveta fishery to rudimentary (if any) systems for the multitude of benthic artisanal fisheries. In 2017, Peru made a public commitment to make Peru’s Vessel Monitoring System (VMS) data publicly available through Global Fishing Watch; in 2018, Ministry of Production (PRODUCE) passed supreme decree DS-003-2018-PRODUCE, calling for strengthened formalization policies and the sustainable development of the artisanal fisheries through improved control and surveillance. The Ocean’s Institute of Peru (IMARPE) is currently in charge of the most comprehensive in-country data collection, housed in the IMARSIS system and analyzed by a separate statistics program. A push for formalization, including boat registration and fisher licensing, is also underway, utilizing online registration systems and electronic databases to store information.
System Attributes:

• Primary Drivers: Sanctions/Trade Barriers (SIMP, EU IUU); NGO or Industry Proof of Concept; On-the Ground NGO Support
• Enabling Conditions: N/A – we were unable to find any significant enabling conditions for data modernization in Peru
• Funding Type: Discretionary; Dedicated
• Leadership: Amplified Voice
• Timeframe: 3 years (Stage 1), 2 years + (Stage 2) = 5 years +

Stakeholder Engagement & Technology Deployment:

• Global Fishing Watch (GFW): As of October 2018, at least 1,300 of Peru’s industrial fishing vessels, most of which were previously undetected by GFW’s Automatic Identification System (AIS) data, are now visible on the public map. The goal is to aid in national monitoring and control efforts, including combating Illegal, Unreported and Unregulated (IUU) fishing.
• WWF TrazApp: WWF Peru is piloting a program to engage fishers to register and sell legal products through TrazApp, an initiative which is taking place in partnership with regional governments and fish landing sites.
• Government of Peru:
• Sistema de seguimiento satelital de embarcaciones (SISESAT): Peru’s Ship Satellite Tracking System, SISESAT, is run by the Pesca Responsable (Responsible Fishing) office, and is used to monitor the spatial distribution of industrial fishing fleets including anchoveta, hake, and squid. The system was implemented in Peru at the beginning of 2001 for the purpose of analyzing fleet distribution dynamics and resource variability. SISESAT use will soon be expanded to mahi mahi, which is an exclusively artisanal fleet.
• Sistema de Trazabilidad Satelital (TRASAT): Created by PRODUCE, TRASAT allows users to visualize the positioning of fishing vessels monitored by satellite. The general service is available to all users, and is published by PRODUCE on a public web portal.
• Monitoreo con senal satelital (SIMTRAC): The Aquatic Traffic Information and Monitoring System (SIMTRAC) uses an AIS system to determine the position, course, and speed of foreign ships. Information is regulated by IMO.
• Imágenes de Píxeles de Luminosidad del Satélite SUOMI: Nocturnal images provided by the Japanese satellite Suomi NPP show the location of the foreign squid fleet operating in international waters adjacent to the Exclusive Economic Zone (EEZ) of Peru.
• SITRAPESCA: Project to automate data produced through disembarkation, processing, transport, and commercialization, including certificates of origin. Created by the General Direction of Supervision, Fines, and Sanctions (DGSFSPA), who is coordinating with other government offices and industry actors.
• IMARSIS: The internal information database of IMARPE, a repository of sample capture and environmental data from along the coast in the form of a web platform that has been tested in the anchoveta fishery, with goals to scale nationally. Landing samples are collected on paper, then transferred to an excel format, which is then shared with the internal IT office to be uploaded into the central database.
• RAPIPEZ: Electronic sales platform created by PRODUCE to help artisanal fishers sell their products locally, designed in part to help fishers adjust to international supply chain disruptions caused by COVID19.

Parties to the Naru Agreement (PNA)

Background: The Parties to the Naru Agreement (PNA) include 8 island nations: the Federated States of Micronesia, Kiribati, Marshall Islands, Nauru, Palau, Papua New Guinea, Solomon Islands and Tuvalu. Innovative and cooperative management measures of PNA’s high-value tuna stock has allowed PNA countries to take ownership of the market through collaboratively managing stock sustainability. The countries jointly utilize Fisheries Information Management System (FIMS) and the integrated Fisheries Information Management System (iFIMS) technology underpinning the Vessel Day Scheme (VDS) in the largest and most complex tuna fisheries management system that currently exists. The PNA FIMS/iFIMS was established specifically to support the implementation of the VDS. iFIMS is a separate and distinct system only for industry, customised at industry request, with ongoing management and development now funded by industry.
System Attributes:

• Project Drivers: Market Differentiations, On-the-Ground NGO support, Cross-Jurisdiction Needs (international)
• Enabling Conditions: Government Assumes Responsibility, Stakeholders Set and Maintain Commitments, Recognition of Broader Benefits Informs Strategy, Minimum Viable Participant Group, Communication and Feedback Loops
• Funding Type: Dedicated, Internal
• Leadership: Amplified voice, Patient Execution, Task-Master, visionary
• Timeframe: Ongoing (Stage 4. Initial stages 1-3 took over a decade)

Stakeholder Engagement & Technology Deployment:

• The PNA Vessel Day Scheme (VDS) sets a Total Allowable Effort (TAE) limit on the number of days fishing vessels are licensed to fish in PNA Exclusive Economic Zones (EEZs) on an annual basis. Each country is allocated an annual share of the TAE for use in its zone, and VDS days can be traded between countries.
• iFIMS is not considered extraordinarily high tech, but it is well developed to fit the needs of the PNA countries. The PNA benefits from their commitment in taking on one system (iFIMS), implementing it, and further making adjustments upon it (i.e. integrating with other platforms, interoperability with other regional systems). The unique requirements and processes for data sharing/reporting that support a harmonized and interoperable system in the PNA region include the following:
  • Purse seine and longline vessels that want to fish in the PNA countries’ EEZs or FZs must register on the Online Vessel Register (OVR) on the PNA website.
  • PNA register is linked to other registers of the region ( i.e. FFA Regional Register and WCPFC Record of FIshing Vessels)
  • Data is transmitted from tablet via satellite to each country’s Fisheries agency and reported through the PNA data center to the FFA and the SPC.

Table 4 Summary of Case Study Drivers, Enabling Conditions, Funding Type, Leadership Type, and Timeframe.