Selected Publications

The Global Deal for Nature (GDN) is a time-bound, science-driven plan to save the diversity and abundance of life on Earth. Pairing the GDN and the Paris Climate Agreement would avoid catastrophic climate change, conserve species, and secure essential ecosystem services. New findings give urgency to this union: Less than half of the terrestrial realm is intact, yet conserving all native ecosystems—coupled with energy transition measures—will be required to remain below a 1.5°C rise in average global temperature. The GDN targets 30% of Earth to be formally protected and an additional 20% designated as climate stabilization areas, by 2030, to stay below 1.5°C. We highlight the 67% of terrestrial ecoregions that can meet 30% protection, thereby reducing extinction threats and carbon emissions from natural reservoirs. Freshwater and marine targets included here extend the GDN to all realms and provide a pathway to ensuring a more livable biosphere.
In Science Advances,2019

While the ecological impacts of fishing the waters beyond national jurisdiction (the high seas) have been widely studied, the economic rationale is more difficult to ascertain because of scarce data on the costs and revenues of the fleets that fish there. Newly compiled satellite data and machine learning now allow us to track individual fishing vessels on the high seas in near real time. These technological advances help us quantify high-seas fishing effort, costs, and benefits, and assess whether, where, and when high-seas fishing makes economic sense. We characterize the global high-seas fishing fleet and report the economic benefits of fishing the high seas globally, nationally, and at the scale of individual fleets. Our results suggest that fishing at the current scale is enabled by large government subsidies, without which as much as 54% of the present high-seas fishing grounds would be unprofitable at current fishing rates. The patterns of fishing profitability vary widely between countries, types of fishing, and distance to port. Deep-sea bottom trawling often produces net economic benefits only thanks to subsidies, and much fishing by the world’s largest fishing fleets would largely be unprofitable without subsidies and low labor costs. These results support recent calls for subsidy and fishery management reforms on the high seas.
In Science Advances,2018

Although fishing is one of the most widespread activities by which humans harvest natural resources and reshape ecosystems, its global footprint is poorly understood and has never been directly observed. We processed 22 billion Automatic Identification System (AIS) messages and tracked >70,000 industrial fishing vessels from 2012-2016, creating a global dynamic footprint of fishing effort with two to three orders of magnitude higher spatial and temporal resolution than previous datasets. Our data show that industrial fishing occurs in >55% of ocean area and has a spatial extent more than 4 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.
In Science,2018

Recent Publications

. A Global Deal For Nature: Guiding principles, milestones, and targets. In Science Advances, 2019.

PDF Website World Economic Forum story

. Leveraging satellite technology to create true shark sanctuaries. In Conservation Letters, 2018.


. Wealthy countries dominate industrial fishing. In Science Advances, 2018.

PDF Project Press release

. The Economics of Fishing the High Seas. In Science Advances, 2018.

PDF Code National Geographic Story TED Talk Blog post

. Rapid and lasting gains from solving illegal fishing. In Nature Ecology & Evolution, 2018.

PDF Code Project

. Tracking the Global Footprint of Fisheries. In Science, 2018.

PDF Code Dataset Project Press release

Recent Posts

What is BigQuery Global Fishing Watch’s data in BigQuery Establishing a connection 1. Summarize the number of vessels by flag state and gear type 2. Make a time series of fishing effort for China’s trawlers fleet The dplyr way Why would I ever write SQL? The SQL way 3. Make a map of fishing effort for a particular region of the ocean Closing remarks This blog post explains a couple of workflows to connect to Big Query using R, and access Global Fishing Watch’s public data.


The dataset Making the dataset spatial Making Great Circles Work Visualizing directionality Final plotting Global Fishing Watch has opened the doors to a new era of transparency and accountability in industrial fisheries. Our recent paper in Science: “Tracking the global footprint of fisheries” introduced this database to the scientific community and highlighted some of the potential research and management applications. One of these is to examine the global network of transnational fisheries (i.



Global Fishing Watch

Illuminating the world’s fishing fleets using satellites and machine learning

Pristine Seas

Working to protect the last wild places in the Ocean


  • Bren School of Environmental Science & Management, 2400 Bren Hall, University of California, Santa Barbara, CA 93106-5131