How these concepts will explicitly address the 3 legs: Human Dimension: this project directly addresses the human dimension of illegal, unreported and unregulated (IUU) fishing, focusing on the dynamic feedbacks between the environment and human behavior. Big Data: this project explores this topic using new sources of Big Data for the spatial patterns of fishing vessels, and changes in the ocean environment. Risk and Uncertainty: this project advances a new theoretical framework for quantifying and explaining risk in marine systems – the femtorisk framework. This femtorisk framwork allows us to synthesize the different Big Datasets, and address the topic of IUU fishing in new and important ways. The faculty mentors for this project have skillsets that span the range of topics for this NRT group: fisheries science, geospatial science, complex systems and biological oceanography.
Faculty Contacts:
James Watson CEOAS; jrwatson@coas.oregonstate.edu
Jamon Van Den Hoek CEOAS; vandenhj@oregonstate.edu
David Wrathall CEOAS; wrathald@oregonstate.edu
Bo Zhao CEOAS; zhao2@oregonstate.edu
Michael Harte CEOAS; mharte@coas.oregonstate.edu
Topic: Assessing risks of conflict due to Illegal, Unreported and Unregulated (IUU); building a global geospatial data base for marine traffic and fisheries; analyzing these “Big Data” using new techniques for improved prediction and understanding of IUU fishing behavior; and modeling the risk and consequences of marine resource-based conflict under contemporary and future climate scenarios.
Background: The oceans are a key source of food and income, and a medium of transport for over a billion people worldwide. Potential risks of large-scale conflict over marine resources exist in numerous highly contested regions including the South China Sea, the Central American Caribbean and Pacific, and the North and South Atlantic. Heightening the risk of conflict is data scarcity and effective risk analysis that undercuts geopolitical decision-making over oceans as a space for competing territorial claims to marine resources. A dimension of conflict of growing interest are commercial and subsistence fishing disputes, and more specifically — Illegal, Unreported and Unregulated fishing. IUU fishing activity is also closely related to the illegal drug trade, smuggling of arms and endangered species and in some cases, human trafficking.
In this NRT group, we will explore the social and economic drivers of IUU fishing fisheries-related conflict at local, regional and international scales. These include levels of economic development, fisheries management capacity, the local balance of domestic versus distant water fishing fleets, and the presence or absence of fishing subsidies, marine protected areas, and fisheries access agreements. These factors influence the risk of conflict between competing fleets, which may cumulatively scale to international conflict. Conversely political changes within countries, including a more aggressive, conflictual or nationalistic foreign policy doctrine, may drive fishery conflict. As such we will explore the fishery “signal” in small- and large-scale international conflict. Risk of conflict may be amplified given uncertainty about future changes in the abundance, distribution and age structure of fish stocks due to harvest exploitation, climate variability and climate change and the interaction of these pressures and stock responses to them.
Existing Data: To quantify IUU fishing as a risk factor in conflict, new Big Data sources are being examined. In particular, the Automatic Identification System (AIS) collects on an hourly basis, the location of tens of thousands of ocean-going vessels. AIS data are passively recorded spatiotemporally adjusted global data on ship location and telemetry, paired with other identifier information such as ships’ nationality, size and cargo. These data can be used to monitor the activities of discrete fishing vessels. At global scales, over long periods of time, AIS data can be used to map the cumulative spatial distribution of fishing effort, and to understand and predict spatio-temporal patterns of marine-system resource exploitation and international competition for fisheries. Understanding these seasonal and historic trends are vital to gauging IUU as a risk factor in marine conflict.
Data Needed: The AIS data are partially in-hand already for the Falklands Exclusive Economic Zone, and we have signed formal data sharing agreements for access to historical AIS data with Exact Earth and Global Fishing Watch, two major AIS data clearing houses. Biophysical and fisheries data are available through active collaborations with OCCRI at OSU and the Seas Around Us Project at UBC. We will estimate error and improve accuracy of AIS–based analysis with targeted marine vessel detection analyses using satellite imagery such as very high resolution (VHR) images (2-3m) from Planet Labs’ Dove satellites. Vessel behavior will then be contextualized with ocean conditions assessed by daily NASA, ESA, and NOAA satellite imagery. Comparing locations derived from AIS and satellite data will give us a comprehensive and entirely new understanding of the spatial behavior of maritime vessels, both fishing and flows of licit and illicit goods and resources. To better understand the competing territorial claims on marine resource access, we will also synthesize data on the location of valuable fish stocks, fishing activity and shipping lanes globally. These data are available through publically accessible sources such as the Seas Around Us project and the RAM Legacy database.
Desired Area(s) of Expertise for Students which complete the three “legs” core concepts: Human Dimension: our students will need expertise in understanding of the dynamic feedbacks between marine resources and resource flows, and human behavior (in particular, behavioral choices that lead to illegal behavior). Big Data: our students will to compile and manage AIS databases and learn and develop analytical models based around AIS data for assessing the risk of conflict (broadly defined) over living marine resources. Big data analytical techniques will include anomaly detection, network analysis, and Bayesian geostatistics. Risk and Uncertainty: our students will have competency in probability theory and some knowledge of stochastic processes and dynamical systems. At the heart of our work will be the new concept of femto-risks, an extension of stochastic dynamical systems thinking to risk and uncertainty in marine systems.
Students who represent the three legs have already been identified with backgrounds in Environmental Science, Biological and Fisheries Oceanography, Marine Ecology, and Geography. We would ideally complement these existing students with those from math (probability, dynamics) and computer science (matrix algebra, Machine Learning).
