2015 Gulf of Mexico Oil Spill & Ecosystem Science Conference

2015 Gulf of Mexico Oil Spill & Ecosystem Science Conference Abstracts of Oral Presentations Session 001 Data Management and Informatics Supporting Ecosystem Sciences Pulling Together Growing Data to Fuel Ecosystem Models: An Introduction to rglobi and ratlantis J. S. Gosnell Baruch College, City University of New York, New York, NY As data related to the Deepwater Horizon Oil Spill and the Gulf of Mexico continues to accumulate in the decade following the spill, there is a growing need for tools and workflows that can allow this information to be synthesized and put to use. While ecosystem models are excellent examples of systems that have the capacity to utilize a wide array of available data in predicting future impacts of the spill, they also demonstrate common issues with dealing with a data source that is constantly evolving. The development of these models is often hampered by the need to access and merge various types of data. This commonly leads to the inability to quickly update or focus models based on new information and limits their application. To address these issues, we describe a set of tools and associated workflow that facilitate the discovery of data from multiple sources and expedite the integration of data into ecosystem models. Currently in development on github, rglobi pulls data on species interactions from the Global Biotic Interactions database, which includes the GoMexSI database. ratlantis offers an R- based interface to the Atlantis ecosystem model source code that employs rglobi and other packages to quickly create and update ecosystem models. These tools automate much of the model development process and offer a new workflow for creating and comparing ecosystem models. I demonstrate the application of this process in developing a new Atlantis model for the eastern Gulf of Mexico. These tools also demonstrate how open science applications and collaborative technologies such as github offer paths to dealing with growing data sources and their implications. Gulf of Mexico Species Interactions (GoMexSI): Building a Baseline Database of Gulf-wide Species Interaction Networks for Perturbation Response Models J. Simons1, J. H. Poelen2, M. Yuan3, M. Vega-Cendejas4, C. Carollo5, D. Reed6, T. Mitchell7, C. Ainsworth8 1Center for Coastal Studies, Texas A&M University-CC, Corpus Christi, TX, 2Data Analysis and Visualization Consultant, Oakland, CA, 3School of Economic, Political & Policy Sciences, University of Texas-Dallas, Richardson, TX, 4Centro de Investigacion de Estudios Avanzados del Instituto Politecnico Nacional, Meridia, Mexico, 5Harte Research Institute, Texas A&M University-Corpus Christi, Corpus Christi, TX, 6Florida Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, St. Petersburg, FL, 7Center for Coastal Studies, Texas A&M University-Corpus Christi, Corpus Christi, TX, 8College of Marine Science, University of South Florida, St. Petersburg, FL Many fishery ecosystem models have been created in the Gulf of Mexico. One significant commonality is their reliance on diet data for species or functional groups being modeled. For a new model, diet data must either be searched for, or use a modified diet matrix used by a previous model. Time constraints usually prevent one from examining all data available for the system of interest. We report on the Gulf of Mexico Species Interaction (GoMexSI) database, built on Neo4j technology, and webpage (gomexsi.tamucc.edu), that can streamline the process of diet data acquisition for future Gulf ecosystem models. While the database aims to include all types of species interactions, we are currently focused on predator-prey interactions of fishes. The database is constructed by extracting species interaction data from historical references and user-contributed datasets, and is managed using Github. Currently the database includes 37,930 interactions from 61 references/contributors, representing 1,346 unique interactors. Users can query data and view summaries online, or they can download raw data downloaded to a csv file. There are also pages for spatial query tools and exploration of predator and prey webs for specific species. We are examining diet data shortfalls through taxonomic and spatial gap analyses and adding new data through stomach content analyses. Many of these data are being targeted for a Gulf-wide Atlantis model, and will be useful for other ecosystem models under development. Cryptographic Hashing of Research Data Files to Ensure Data Integrity J. C. Davis, M. S. Williamson Texas A&M University-Corpus Christi, Corpus Christi, TX Today, most scientific research data is recorded in electronic format, which not only facilitates analysis, but also data sharing. To facilitate data sharing, there exist a number of repositories for electronic scientific data, such as the National Oceanographic Data Center (NODC), Dryad, and the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC). An important issue data providers, data consumers, and the repositories themselves face is how to ensure integrity of the electronic data as it is passed from the provider, to the repository (and within the repository), and ultimately to the consumer. The assurance of data integrity is important, not only for scientific, but also for legal reasons, especially in oil spill-related research, where findings could have an economic and/or legal impact. Cryptographic hashing is a common method used to ensure the contents of electronic files have not been altered in any way. This talk will discuss using cryptographic hashing as a means of ensuring data integrity, along with novel methods for generating these hashes efficiently for large data files using freely available tools. It will also discuss methods of recording these hashes in common metadata formats (such as ISO 19115-2) and using them as a part of or even as a possible alternative to the Digital Object Identifier (DOI) System. Enabling Near-Real-Time Mitigation with Web Services for Sharing Buoy Data: Potential Applications for Marine Mammal Monitoring in the Gulf of Mexico M. Robbins, H. Cheyne Cornell University, Ithaca, NY The Bioacoustics Research Program at the Cornell Lab of Ornithology has developed a system for near- real-time marine acoustic monitoring, enabling immediate mitigation of anthropogenic activities pursuant to environmental regulations by providing a framework for shore-side clients to assess noise and monitor animal presence. The system consists of a buoy-mounted electronics package that communicates with a shore-side server via Iridium satellite or GSM/GPRS. The server parses received reports from the buoys into a series of file assets (audio clip files and spectrogram preview images) stored on scalable storage, and metadata (date/time, score, feature vector, algorithm, GPS location, battery voltage, and other status information) inserted into a relational database. These data are then made available via Apache, PHP and Django by a number of web and data interfaces. Analysts are notified by either email or text message about detections that can be reviewed remotely using a custom web interface that includes spectrogram views, audio playback, and annotation capability. Timely information can be distributed to mariners, researchers, and others via Flash-based web interfaces (see www.listenforwhales.org), KML/Google earth, email reports, phone calls, text messages, and AIS broadcasts. Already successfully deployed on the U.S. East Coast and in the Arctic, the system has the potential to offer near-real-time data to oil spill or seismic activity mitigation in the Gulf of Mexico. US IOOS Data Management Services to Address Biological and Ecosystem Data Integration to Support Ecosystem Sciences in the Gulf of Mexico H. Moustahfid1, M. Howard2, V. Subramanian3, P. Goldstein4, H. Brown5 1NOAA/US IOOS, Silver Spring, MD, 2Gulf of Mexico Ocean Observing System Regional Associations (GCOOS-RA), Texas A&M University, College Station, TX, 3Southeast Coastal Ocean Observing Regional Association (SECOORA), St Petersburg, FL, 4University of Colorado/Ocean Biogeographic Information System-USA, Boulder, CO, 5NOAA/SEFSC, Galveston, TX An important Data Management and Communication (DMAC) goal is to enable a multi-disciplinary view of the ocean environment by facilitating discovery and integration of data from various sources, projects and scientific domains. United States Integrated Ocean Observing System (U.S. IOOS) DMAC functional requirements are based upon guidelines for standardized data access services, data formats, metadata, controlled vocabularies, and other conventions. So far, the data integration effort has focused on geophysical U.S. IOOS core variables such as temperature, salinity, ocean currents, etc. The IOOS Biological and Ecosystem Observations Services are addressing the DMAC requirements that pertain to biological and ecosystem observations standards and interoperability applicable to U.S. IOOS and to various observing systems. Biological and ecosystem observations are highly heterogeneous and the variety of formats, logical structures, and sampling methods create significant challenges. Here we describe an informatics framework for biological observing data that is already expanding information content and reconciling standards for the representation and integration of these biological observations for users to maximize the value of these observing data. We further demonstrate an initial implementation in the Gulf of Mexico Ocean Observing

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