Contacts:

  • Lorenzo Ciannelli, Oregon State University (OSU): lciannel@coas.oregonstate.edu
  • Fuxin Li, OSU: lif@oregonstate.edu
  • Jake Hamblin, OSU: jacob.hamblin@oregonstate.edu

Topic:

What promises do big data bring to science and management of marine coastal ecosystem? Will modern monitoring and big-data analytical techniques yield information that supplement those derived by traditional sampling? Can big data inspire scientists, managers, and the general public?

Background:

Big data abound in marine environments. They stream from an array of sources including: ocean sensors, satellites, genetic analyses, ocean modeling, and in-situ measurements. Big data, if used well could greatly increase productivity and provide more information to marine science, management and policy making. However, they are difficult to analyze, and also hard to conceptualize and communicate to academic, agencies, and the general public. These challenges may impose limits to their use and applications, if not addressed. In this study, we bring together expertise from oceanography, computer science and arts and humanities to develop a case study around the analyses and communication of big data for scientific, management and outreach purposes. The study is based on the monitoring of soft sediment marine habitats in nearshore waters of central Oregon. Surveys of these habitats have taken place since 2008 and integrated traditional sampling techniques based on trawl catches, with modern techniques based on in-situ video observations of microhabitat characteristics, species assemblages, and individual fish behaviors. Coastal fish communities in soft sediment habitats are very poorly described in the OR coast. These coastal habitats are nursery areas for commercially fish and invertebrate species. It is therefore important to characterize these habitats and associated communities in our coastal areas.  Given the multiple uses of the OR coastal zone (fishing, recreation, energy production), such characterization can help formalize and implement marine spatial planning initiatives aimed at protecting local biota, while maintaining economically vibrant coastal communities. New sensor and data technologies open up an opportunity to monitor and manage these habitats, but as long as their uses remain confined within disciplinary and academic boundaries they will have limited reach. We are thus missing a narrative framework to conceptualize the myriad ways big data have motivated scientists and the questions they have asked.

NRT Project Goals:

The overarching goal of this project is to study practices for integrating big data in monitoring and management of coastal marine ecosystems. To pursue this goal we will:

  1. Develop machine-learning techniques for automated analyses of video data from benthic habitats
  2. Make ecological inference about fish habitat use and quality based on analyses of video and trawl data
  3. Develop a historically-informed narrative about the past use and contemporary promise of big data analytics, as related to marine science and fisheries, drawing on techniques of data visualization and artistic rendering.

Existing Data:

Since summer 2008 a team of scientists at NOAA and OSU have sampled nearshore juvenile fish assemblages along the NH line using trawling and video techniques. Starting in 2012, sampling has occurred monthly. This time frame includes some of the most extreme years that we have seen in our coastline. These modern collections use the same protocol (gear, stations, and time) of 1970s collections, making the two data sets comparable, and opening up an opportunity to study the impact of technological developments on the study of marine systems.

Desired Area(s) of expertise for students:

  • Human dimensions: The HD component include expertise form arts and humanities for developing historically-informed narratives about past and contemporary use of big data, especially modes of visualization and artistic rendering, in science and management
  • Bid data dimension: The BD component of this project will be the automatic segmentation and counting analyses of the in-situ video based on machine learning
  • Earth system dimension: The ES includes expertise in fisheries, oceanography and ecology for making ecological inference about fish habitat use and quality
  • Policy and management: The PM component include expertise to develop new management strategies and monitoring systems based primarily on the use of big data
  • Mathematics and statistics: The MS component include expertise to model and make ecological and policy inference based on the use of big data

 

 

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