Apparent fishing events (AIS)

Overview

The automatic identification system (AIS) is a tracking system developed for maritime safety and collision avoidance. Global Fishing Watch collects and analyzes AIS data transmitted by vessels to understand global vessel activity. AIS data includes a vessel’s identity, location, speed, direction, and more, collected through a global network of satellite and terrestrial receivers. Global Fishing Watch processes billions of AIS messages daily, focusing on vessels likely to be fishing based on their movement patterns and registry information. Using machine learning algorithms, Global Fishing Watch classifies (Kroodsma et al. 2018) vessel type and infers fishing activity from features such as changes in speed and direction. Apparent fishing events are created by aggregating individual apparent fishing effort data points into grouped “events,” making the data easier to visualize and analyze. An apparent fishing event is defined where:
  • Fishing positions appear consecutively and are separated by less than 10 kilometers or 2 hours; and,
  • Fishing positions within 1 hour and 2 kilometers of another fishing event are grouped together into a single event.
The dataset is further restricted by removing fishing events that are brief and fast, as these are less likely to indicate a realistic fishing event. The following short fishing events are removed:
  • Events less than 20 minutes in duration;
  • Events comprised of five or fewer fishing positions;
  • Events that cover distance of less than 0.5 km (for all gears except estimated squid gear);
  • Events that cover distance of less than 50m (for estimated squid gear); and,
  • Abnormally fast moving vessel events with an average vessel speed of 10 knots or greater.

AIS Limitations

  • Not all vessels carry AIS transponders: AIS is mainly used by vessels over 24 meters that operate further from shore. Many small-scale or artisanal vessels do not carry AIS and subsequently are not captured in the AIS-based apparent fishing effort layer.
  • Inconsistent transmission: AIS broadcasts are more frequent and detectable with Class A devices, which are common on larger vessels. Class B devices (used by smaller vessels) have weaker signals and are received less reliably, especially by satellite. Where a vessel inconsistently transmits, the vessel positions that are predicted as fishing may have a disproportionate amount of time associated with them, which can lead to over or under predictions of fishing effort.
  • Signal interference and reception gaps: In dense vessel traffic areas, AIS signals can overlap and interfere, reducing satellite detection of individual transmissions. 
  • Deliberate disabling or falsification: AIS can be turned off or deliberately spoofed, leading to false locations or shared identities.
  • Data quality and update lag: Not all providers capture or share complete AIS datasets. Terrestrial stations may log messages less frequently, and satellite data coverage is uneven.
  • Interpretation over time: Increases or decreases in apparent activity could reflect changing AIS usage, changing access to terrestrial stations or dynamic AIS, or reception quality rather than actual changes in fishing behavior. When doing a long-term study on fishing activity trends with our data, we suggest contacting us to help with the interpretation.

Caveats

  • Interpreting apparent fishing events: Our general fishing model estimates fishing-related activity, which includes more than just moments when gear is in the water. Apparent fishing events are groupings of individual fishing effort positions and are intended to reflect a range of fishing-related behaviors—such as searching or preparing gear—not just gear deployment or retrieval. These events should not be interpreted as precise records of gear setting or hauling. Additionally, because the grouping rules are not gear-specific and do not adjust for AIS signal quality, some positions may be grouped together arbitrarily.
  • Bias in vessel identification and gear classification: Misclassifications in vessel type may occur due to inconsistent or incomplete vessel registry data. Misclassifications can happen when algorithms struggle to appropriately categorize vessels, for instance, where vessels use several gears (thus changing their behavioral patterns) or when a vessel’s MMSI (maritime mobile service identity) number is used by more than one vessel, then MMSI recycling may result in misclassification of vessel type by our vessel classification model. Misclassification of vessel type can result in the unexpected presence or absence of vessels in the apparent fishing event estimates.
  • Apparent fishing events vs apparent fishing effort: Apparent fishing events group consecutive apparent fishing positions into summarized events for easier visualization. Filters based on time, distance and vessel behavior, mentioned above, can exclude some fishing positions. As a result, apparent fishing events and apparent fishing effort may differ, especially when fishing activity is spread out over long distances or time gaps.
  • Apparent fishing event location: Our event APIs determine a single location for each fishing event by calculating the average latitude and longitude of all positions within that event. As a result, this point may not align exactly with the vessel’s track, and it’s possible the vessel wasn’t at that precise location during the event.

License

Non-Commercial Use Only. The Site and the Services are provided for Non-Commercial use only in accordance with the CC BY-NC 4.0 license. If you would like to use the Site and/or the Services for commercial purposes, please contact us.

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