Tracking the global footprint of fisheries

Abstract

Although fishing is one of the most widespread activities by which humans harvest natural resources, its global footprint is poorly understood and has never been directly quantified. We processed 22 billion automatic identification system messages and tracked >70,000 industrial fishing vessels from 2012 to 2016, creating a global dynamic footprint of fishing effort with spatial and temporal resolution two to three orders of magnitude higher than for previous data sets. Our data show that industrial fishing occurs in >55% of ocean area and has a spatial extent more than four times that of agriculture. We find that global patterns of fishing have surprisingly low sensitivity to short-term economic and environmental variation and a strong response to cultural and political events such as holidays and closures.

Catching industrial fishing incursions into inshore waters of Africa from space

“…This source of uncertainty is not captured herein, but means that the data at hand is conservative as more vessels operate without switching their AIS on (Figure S1). Second, although the algorithm identifying which vessels are fishing has an overall accuracy over 90% (Kroodsma et al, ), it does incorrectly label some fishing effort. For this study, the key uncertainty is the number of positions within the restricted areas that are incorrectly labelled as fishing locations, as opposed to false positives associated with vessels visiting ports.…”

“…In this paper, we assess the spatial distribution of industrial fishing fleets within African coastal waters by using AIS data‐estimated fishing effort and evaluating the potential extent of industrial fishing fleets’ operations in inshore areas, where most small‐scale fishing activities take place. We used the AIS data‐predicted fishing effort provided by Global Fishing Watch (GFW), a non‐profit research platform which uses a convolutional neural network, a form of machine learning, to predict when vessels are engaged in fishing activity based on their movements (Kroodsma et al, ). We reviewed the legal definition of inshore zones as a proxy for where small‐scale fishers are operating, and then calculated the hours of industrial‐scale fishing in these inshore areas and relative contribution of different flag states.…”

Ocean frontiers: epistemologies, jurisdictions, commodifications

“…Satellite data on the location of fishing vessels and fishing‐related activities has recently been made public, and research activity considering how such data can be applied for governance purposes has quickly followed (e.g., Kroodsma et al . ; Toonen and Bush ). Local‐scale knowledge production networks aim to record local and/or traditional knowledge to enhance baseline understanding and characterisations of culturally and ecologically important coastal species and processes, an often controversial process that can be associated with appropriation (Oguamanam ).…”

Evolution of marine fisheries management in China from 1949 to 2019: How did China get here and where does China go next?

“…China has the world’s largest marine fishing fleet; the fishing vessel number accounted for 19% of the world’s total (FAO, ), making China’s nearshore and offshore waters the busiest fishing area in the world (Kroodsma et al, ). However, the fleet operates with poor fishing selectivity (Figure S3) and a relatively low level of per capita productivity; the production per fisher in China was 1.89 tons in 2014, compared with 24.2 tons in Europe, 19.7 tons in North America, 10.4 tons in Oceania and 2.47 tons for the global average (FAO, ).…”