Global Fishing Watch: Bringing Transparency to Global Commercial Fisheries

In recent years, however, there have been attempts to protect marine ecosystems and biodiversity by creating large marine protected areas and developing a framework for a global treaty to better manage those areas and the resources within them on the high seas 3,4 . Historically, these efforts have been constrained by humanity’s lack of capacity to effectively monitor and enforce non-compliance in such vast areas, but a fast-growing publically accessible source of ship-tracking data known as AIS has potential to change that. Vessels broadcast and receive AIS signals to avoid colliding with each other.

Satellite-based receivers can also pick up these signals, and AIS is increasingly being viewed not only as an important maritime safety system, but as an equally important tool for monitoring and enforcing our oceans to diminish illegal activity. One major sector that could benefit from this new application of AIS data is fisheries.

In 2009, a report estimated the annual economic and biological impact of Illegal, Unreported and Unregulated fishing (IUU) on global fisheries to be a $10-23.5 billion loss in revenue from an illegal harvest of 11-26 million tonnes of fish 3 . The report also found that developing countries and Small Island Developing States (SIDS) were more prone to IUU activity due to an inability to govern their sovereign waters. For example, estimated catches in West African and Western Central Pacific Exclusive Economic Zones (EEZs), were 37% and 34%, higher than what was actually being reported due to lack of enforcement of foreign fishing fleet license and Bloomberg Data for Good Exchange Conference. 25-Sep-2016, New York City, NY, USA.

reporting requirements. These estimates not only represent a major loss of information necessary to conduct proper scientific assessments of the health of fish stocks in the region, but also an economic and biological loss for these nations. Most of the catch is landed elsewhere and in contravention of sustainable fisheries practices designed to ensure future conservation of economically important fisheries 4 .

The solution to resolving the problem of IUU fishing is multi-faceted, but the single most important component begins with fisheries monitoring. Global Fishing Watch, a product of a partnership between Oceana, SkyTruth, and Google, is making the impossible, possible, by providing the first open-source global near-real time record of apparent fishing effort. Citizens can use it to see for themselves whether their fisheries are being effectively managed, and hold their leaders accountable for long-term fishery sustainability. Seafood suppliers can keep tabs on the vessels from which they buy fish. Journalists and the public can act as watchdogs to improve the sustainable management of global fisheries. Responsible fishermen can use Global Fishing Watch to show they are adhering to the law and researchers can access a multi-year record of all apparent fishing activity to conduct applied scientific research and thus policy goals for marine conservation. Hundreds of millions of people around the world depend on our ocean for their livelihoods, and many more rely on it for food. Collectively, the various applications of Global Fishing Watch will help reduce overfishing and illegal fishing and help restore the ocean’s abundance and ensure sustainability through better monitoring and governance of our marine resources.

Global Fishing Watch harnesses data collected from land and satellite-based receivers. AIS signals, emitted as frequently as several times per minute, include the position, speed, heading, vessel identity, and other information. Our AIS database spans January 1, 2012, until the present and contains more than 20 billion AIS messages processed from more than 200,000 ocean going vessels. To examine fishing vessels contained within this database, Global Fishing Watch developed a behavioral classification model to identify when and where vessels appear to be engaged in fishing activity. The output of the classification model is apparent fishing activity, measured in the number of hours each vessel spent engaged in apparent fishing activity, displayed on an interactive map. The fishing vessels displayed on the interactive map have been classified into three categories: (1) known fishing vessels; (2) likely fishing vessels; (3) suspected fishing vessels. Known fishing vessels have been matched by Marine Maritime Service Identity (MMSI) number to unique vessel identifiers such as International Maritime Organization (IMO), Common List of Authorized Vessel (CLAV) number, International Radio Callsign (IRCS), ship name, and gear type from major vessel registries (e.g., CLAV list, Regional Fisheries Management Organization lists, etc.). Likely fishing vessels are vessels that selfidenti