Kristina Boerder

Ending hide and seek at sea

New technologies could revolutionize ocean observation The ocean remains the least observed part of our planet. This deficiency was made obvious by two recent developments in ocean governance: the emerging global movement to create massive marine protected areas (MPAs) (1) and a new commitment by the United Nations (UN) to develop a legally binding treaty to better manage high-seas biodiversity (2). Both policy goals cause us to confront whether it is meaningful to legislate change in ocean areas that we have little capacity to observe transparently. Correspondingly, there has been a surge in interest in the potential of publicly accessible data from automatic ship identification systems (AIS) to fill gaps in ocean observation. We demonstrate how AIS data can be used to empower and propel forward a new era of spatially ambitious marine governance and research. The value of AIS, however, is inextricably linked to the strength of policies by which it is backed.

Tracking the global footprint of fisheries

More than half the fish in the sea As the human population has grown in recent decades, our dependence on ocean-supplied protein has rapidly increased. Kroodsma et al. took advantage of the automatic identification system installed on all industrial fishing vessels to map and quantify fishing efforts across the world (see the Perspective by Poloczanska). More than half of the worlds oceans are subject to industrial-scale harvest, spanning an area four times that covered by terrestrial agriculture. Furthermore, fishing efforts seem not to depend on economic or environmental drivers, but rather social and political schedules. Thus, more active measures will likely be needed to ensure sustainable use of ocean resources. Science, this issue p. 904; see also p. 864 More than half of the ocean is exposed to industrial fishing activities. 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.

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011–2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

The environmental niche of the global high seas pelagic longline fleet

The distribution of global high seas longline fishing is predictable across space and time using environmental variables. International interest in the protection and sustainable use of high seas biodiversity has grown in recent years. There is an opportunity for new technologies to enable improvements in management of these areas beyond national jurisdiction. We explore the spatial ecology and drivers of the global distribution of the high seas longline fishing fleet by creating predictive models of the distribution of fishing effort from newly available automatic identification system (AIS) data. Our results show how longline fishing effort can be predicted using environmental variables, many related to the expected distribution of the species targeted by longliners. We also find that the longline fleet has seasonal environmental preferences (for example, increased importance of cooler surface waters during boreal summer) and may only be using 38 to 64% of the available environmentally suitable fishing habitat. Possible explanations include misclassification of fishing effort, incomplete AIS coverage, or how potential range contractions of pelagic species may have reduced the abundance of fishing habitats in the open ocean.

Global hot spots of transshipment of fish catch at sea

Satellite tracking of global transfer of catch from fishing boats to cargo vessels enables better tracing of seafood supply chains. A major challenge in global fisheries is posed by transshipment of catch at sea from fishing vessels to refrigerated cargo vessels, which can obscure the origin of the catch and mask illicit practices. Transshipment remains poorly quantified at a global scale, as much of it is thought to occur outside of national waters. We used Automatic Identification System (AIS) vessel tracking data to quantify spatial patterns of transshipment for major fisheries and gear types. From 2012 to 2017, we observed 10,510 likely transshipment events, with trawlers (53%) and longliners (21%) involved in a majority of cases. Trawlers tended to transship in national waters, whereas longliners did so predominantly on the high seas. Spatial hot spots were seen off the coasts of Russia and West Africa, in the South Indian Ocean, and in the equatorial Pacific Ocean. Our study highlights novel ways to trace seafood supply chains and identifies priority areas for improved trade regulation and fisheries management at the global scale.

Response to Comment on “Tracking the global footprint of fisheries”

Amoroso et al. demonstrate the power of our data by estimating the high-resolution trawling footprint on seafloor habitat. Yet we argue that a coarser grid is required to understand full ecosystem impacts. Vessel tracking data allow us to estimate the footprint of human activities across a variety of scales, and the proper scale depends on the specific impact being investigated.

MARINE GOVERNANCE. Ending hide and seek at sea.

New technologies could revolutionize ocean observation The ocean remains the least observed part of our planet. This deficiency was made obvious by two recent developments in ocean governance: the emerging global movement to create massive marine protected areas (MPAs) (1) and a new commitment by the United Nations (UN) to develop a legally binding treaty to better manage high-seas biodiversity (2). Both policy goals cause us to confront whether it is meaningful to legislate change in ocean areas that we have little capacity to observe transparently. Correspondingly, there has been a surge in interest in the potential of publicly accessible data from automatic ship identification systems (AIS) to fill gaps in ocean observation. We demonstrate how AIS data can be used to empower and propel forward a new era of spatially ambitious marine governance and research. The value of AIS, however, is inextricably linked to the strength of policies by which it is backed.

Ending hide and seek at sea - eScholarship

New technologies could revolutionize ocean observation The ocean remains the least observed part of our planet. This deficiency was made obvious by two recent developments in ocean governance: the emerging global movement to create massive marine protected areas (MPAs) (1) and a new commitment by the United Nations (UN) to develop a legally binding treaty to better manage high-seas biodiversity (2). Both policy goals cause us to confront whether it is meaningful to legislate change in ocean areas that we have little capacity to observe transparently. Correspondingly, there has been a surge in interest in the potential of publicly accessible data from automatic ship identification systems (AIS) to fill gaps in ocean observation. We demonstrate how AIS data can be used to empower and propel forward a new era of spatially ambitious marine governance and research. The value of AIS, however, is inextricably linked to the strength of policies by which it is backed.

Correction: Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning.

[This corrects the article DOI: 10.1371/journal.pone.0158248.].