Publications by authors named "David A Kroodsma"

Illegal, unreported, and unregulated (IUU) fishing costs billions of dollars per year and is enabled by vessels obfuscating their identity. Here, we combine identities of ~35,000 vessels with a decade of GPS data to provide a global assessment of fishing compliance, reflagging patterns, and fishing by foreign-owned vessels. About 17% of high seas fishing is by potentially unauthorized or internationally unregulated vessels, with hot spots of this activity in the west Indian and the southwest Atlantic Oceans.

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Because many vessels use the Automatic Identification System (AIS) to broadcast GPS positions, recent advances in satellite technology have enabled us to map global fishing activity. Understanding of human activity at sea, however, is limited because an unknown number of vessels do not broadcast AIS. Those vessels can be detected by satellite-based Synthetic Aperture Radar (SAR) imagery, but this technology has not yet been deployed at scale to estimate the size of fleets in the open ocean.

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Marine traffic is increasing globally yet collisions with endangered megafauna such as whales, sea turtles, and planktivorous sharks go largely undetected or unreported. Collisions leading to mortality can have population-level consequences for endangered species. Hence, identifying simultaneous space use of megafauna and shipping throughout ranges may reveal as-yet-unknown spatial targets requiring conservation.

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Illegal, unreported, and unregulated fishing threatens resource sustainability and equity. A major challenge with such activity is that most fishing vessels do not broadcast their positions and are "dark" in public monitoring systems. Combining four satellite technologies, we identify widespread illegal fishing by dark fleets in the waters between the Koreas, Japan, and Russia.

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Many species of sharks and some tunas are threatened by overexploitation, yet the degree of overlap between industrial fisheries and pelagic fishes remains poorly understood. Using satellite tracks from 933 industrial fishing vessels and predictive habitat models from 876 electronic tags deployed on seven shark and tuna species, we developed fishing effort maps across the northeast Pacific Ocean and assessed overlap with core habitats of pelagic fishes. Up to 35% of species' core habitats overlapped with fishing effort.

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Amoroso 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.

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Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining's extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005.

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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.

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To better understand agricultural carbon fluxes in California, USA, we estimated changes in soil carbon and woody material between 1980 and 2000 on 3.6 x 10(6) ha of farmland in California. Combining the CASA (Carnegie-Ames-Stanford Approach) model with data on harvest indices and yields, we calculated net primary production, woody production in orchard and vineyard crops, and soil carbon.

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