Title

Global Atlas of AIS-based fishing activity — Challenges and opportunities

Publication Date

2019

Security Theme

IUU Fishing

Keywords

AIS, automatic indentification system, industrial fishing vessels, AIS global data

Description

"The Automatic Identification System (AIS) provides detailed tracks of tens of thousands of industrial fishing vessels, and these detailed tracking data have the potential to provide estimates of fishing activity and effort in near real time. Realizing this potential, though, is not straightforward and depends on the vessel size, gear type, and the species targeted. This Atlas, using a global database of AIS data from 2017, assesses this potential and shows that AIS can start to be considered a valid technology for estimating fishery indicators. This Atlas reveals both promising findings and key limitations of inferring fishing effort from AIS data. AIS use is steadily increasing and its utility in tracking fishing vessel activity is growing. In 2017, AIS was broadcast by approximately 60 000 fishing vessels of which just over 22 000 could be matched to publicly available vessel registries. This number is steadily increasing, and between 2014 and 2017, the number of vessels broadcasting increased by 10 to 30 percent each year. Moreover, AIS can be used to track the majority of the world’s large fishing vessels (above 24 m), especially those from upper and middle-income countries and territories, distant water fleets and vessels operating on the high seas. AIS tracking performs less well on smaller vessels: only a small fraction of vessels under 24 m, which account for the vast majority of fishing vessels globally, use AIS. The current algorithms perform well at classifying the most common gear types among larger vessels: longlines, trawls and pelagic purse seines. The classification algorithms do less well at differentiating gear types that are more common in smaller coastal vessels, such as set gillnets, trollers and pots and traps. Also, the current AIS algorithms can assign only one gear type, limiting the ability to classify the type of fishing when vessels change gears on a voyage or between voyages. Poor AIS reception limits the ability to monitor fleets in some regions. In particular, satellite AIS reception is weakest in Southeast Asia, followed by East Asia, the northern Indian Ocean, the Gulf of Mexico and Europe, although terrestrial receivers along coastlines can, in some of these regions, compensate for poor satellite reception. The reception quality also depends on the specific type of AIS device in use (Class A or B). Comparing AIS-based fishing vessel activity with catch reconstructions and literature reveals varying use of AIS by region and gear and possible biases in the relative importance of different gears. Catch reconstructions mostly show that areas with high catch have high activity by vessels with AIS, although some areas with high catch have little AIS activity, such as in Southeast Asia (Area 71), as a result of few vessels having AIS. Catch reconstructions agree on ix x the most important gears worldwide (trawlers, followed by purse seiners), although AIS data show a higher importance of longliners because a higher fraction of these broadcast AIS. The recent increasing importance of squid jiggers in the high seas was not captured in the slightly lagged catch reconstruction work. In optimal conditions where AIS use and reception are good, and where vessel registries with gear type exist AIS algorithm can perform well for gears such as longline or trawl and provide good estimates of fishing effort. This work has contributed to improving the quality of FAO fleet statistics, revealed some errors in classifications of gear types in the European Union (EU) registry, and pinpointed limitations of catch reconstructions. With regard to the AIS data, in addition to showing limitations of AIS, this project has helped improve methods for analysing AIS data and align AIS-based metrics with fishery statistical standards, and this work can provide a basis for further improvement of these methods and algorithms."

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Global Atlas of AIS-based fishing activity — Challenges and opportunities

"The Automatic Identification System (AIS) provides detailed tracks of tens of thousands of industrial fishing vessels, and these detailed tracking data have the potential to provide estimates of fishing activity and effort in near real time. Realizing this potential, though, is not straightforward and depends on the vessel size, gear type, and the species targeted. This Atlas, using a global database of AIS data from 2017, assesses this potential and shows that AIS can start to be considered a valid technology for estimating fishery indicators. This Atlas reveals both promising findings and key limitations of inferring fishing effort from AIS data. AIS use is steadily increasing and its utility in tracking fishing vessel activity is growing. In 2017, AIS was broadcast by approximately 60 000 fishing vessels of which just over 22 000 could be matched to publicly available vessel registries. This number is steadily increasing, and between 2014 and 2017, the number of vessels broadcasting increased by 10 to 30 percent each year. Moreover, AIS can be used to track the majority of the world’s large fishing vessels (above 24 m), especially those from upper and middle-income countries and territories, distant water fleets and vessels operating on the high seas. AIS tracking performs less well on smaller vessels: only a small fraction of vessels under 24 m, which account for the vast majority of fishing vessels globally, use AIS. The current algorithms perform well at classifying the most common gear types among larger vessels: longlines, trawls and pelagic purse seines. The classification algorithms do less well at differentiating gear types that are more common in smaller coastal vessels, such as set gillnets, trollers and pots and traps. Also, the current AIS algorithms can assign only one gear type, limiting the ability to classify the type of fishing when vessels change gears on a voyage or between voyages. Poor AIS reception limits the ability to monitor fleets in some regions. In particular, satellite AIS reception is weakest in Southeast Asia, followed by East Asia, the northern Indian Ocean, the Gulf of Mexico and Europe, although terrestrial receivers along coastlines can, in some of these regions, compensate for poor satellite reception. The reception quality also depends on the specific type of AIS device in use (Class A or B). Comparing AIS-based fishing vessel activity with catch reconstructions and literature reveals varying use of AIS by region and gear and possible biases in the relative importance of different gears. Catch reconstructions mostly show that areas with high catch have high activity by vessels with AIS, although some areas with high catch have little AIS activity, such as in Southeast Asia (Area 71), as a result of few vessels having AIS. Catch reconstructions agree on ix x the most important gears worldwide (trawlers, followed by purse seiners), although AIS data show a higher importance of longliners because a higher fraction of these broadcast AIS. The recent increasing importance of squid jiggers in the high seas was not captured in the slightly lagged catch reconstruction work. In optimal conditions where AIS use and reception are good, and where vessel registries with gear type exist AIS algorithm can perform well for gears such as longline or trawl and provide good estimates of fishing effort. This work has contributed to improving the quality of FAO fleet statistics, revealed some errors in classifications of gear types in the European Union (EU) registry, and pinpointed limitations of catch reconstructions. With regard to the AIS data, in addition to showing limitations of AIS, this project has helped improve methods for analysing AIS data and align AIS-based metrics with fishery statistical standards, and this work can provide a basis for further improvement of these methods and algorithms."