Award Abstract # 2137582
NSF Convergence Accelerator Track E: Innovative Seafood Traceability Network for Sustainable Use, Improved Market Access, and Enhanced Blue Economy

NSF Org: ITE
Innovation and Technology Ecosystems
Recipient: LOYOLA MARYMOUNT UNIVERSITY
Initial Amendment Date: September 20, 2021
Latest Amendment Date: September 20, 2021
Award Number: 2137582
Award Instrument: Standard Grant
Program Manager: Aurali Dade
adade@nsf.gov
 (703)292-7468
ITE
 Innovation and Technology Ecosystems
TIP
 Dir for Tech, Innovation, & Partnerships
Start Date: October 1, 2021
End Date: September 30, 2023 (Estimated)
Total Intended Award Amount: $749,599.00
Total Awarded Amount to Date: $749,599.00
Funds Obligated to Date: FY 2021 = $749,599.00
History of Investigator:
  • Demian Willette (Principal Investigator)
    Demian.Willette@lmu.edu
  • Ian Gleadall (Co-Principal Investigator)
  • Warwick Sauer (Co-Principal Investigator)
  • Hassan Moustahfid (Co-Principal Investigator)
  • Cheryl Ames (Co-Principal Investigator)
Recipient Sponsored Research Office: Loyola Marymount University
1 LMU DR
LOS ANGELES
CA  US  90045-2650
(310)338-4599
Sponsor Congressional District: 36
Primary Place of Performance: Loyola Marymount University
1 LMU Drive
Los Angeles
CA  US  90045-2659
Primary Place of Performance
Congressional District:
36
Unique Entity Identifier (UEI): MQSXELH2KMB6
Parent UEI:
NSF Program(s): Convergence Accelerator Resrch
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 131Y
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.083

ABSTRACT

Proposal # 2137582?NSF Convergence Accelerator Track E: Innovative seafood traceability network for sustainable use, improved market access, and enhanced blue economy



Addressing the global challenge of feeding the growing human population will require a solution from the ocean. To prevent reoccurrences of overfishing and its negative ecological impacts, major fisheries require reimagined monitoring and management strategies. Leveraging leading-edge machine learning computer technology and environmental DNA (an organism?s DNA that can be detected in the water) techniques, this project builds a broad network to implement a powerful traceability tool for marine fisheries. This project focuses on octopus (known as cephalopods), a fishery currently at severe risk due to unsustainable exploitation as an animal protein source. While the United States catches a fair number of octopus to supply the domestic market, it also relies on imports from both neighboring and distant countries. Regardless of origin, octopus products on the market should meet the same requirements of other seafoods to ensure consumer protection and fishery sustainability and reduced illegal fishing practices. This project will develop and pilot reliable tools to achieve these goals for the American market and consumers. Deliverables will also help to combat the fraudulent practice of species substitution ? the dishonest labeling and selling of a cheap species under the name of an expensive one. Proper, easy to deploy and affordable environmental DNA traceability techniques will help combat this practice that damages the seafood management chain. Most importantly, this project will help make these tools and techniques available and affordable for octopus-exporting countries, thus allowing for critical check points through the supply chain ? from the fishing net to the dinner plate. From a development perspective, it can help small-scale octopus harvesters in developing countries access the lucrative American market without facing tariff barriers to trade. This will promote fair trade practices. Ultimately, this project applies convergence research concepts that integrate knowledge, methods, and expertise across disciplines to advance science and lay the foundation for solving the simultaneous global challenges of food security, sustainable consumption, and marine resource conservation.

Cephalopods are currently undergoing accelerating misuse and mismanagement with octopus species particularly vulnerable due to their exploitation as an important animal-derived protein. This problem originates from a dearth of data on octopus recruitment and a general lack of infrastructure within the fishery. Hence, the utility of traceability methods rooted in real-time detection and reliable predictions offer promise to robustly assess stocks and their potential for exploitation in the octopus seafood supply chain. Seafood traceability methods must be easily replicable and affordable for the management of seafood to control Illegal, Unreported and Unregulated fishing. Furthermore, transparent activities between the producers and the consumers will facilitate data collection under proper regulations and, ultimately, appropriate decisions towards stock improvements. This NSF Convergence Accelerator project will: (1) Develop a dashboard prototype traceability tool that allows affordable identification of species and area of capture for wild octopus fisheries within the United States and abroad using a machine learning model ?SeaTraceBlueNet? trained on legacy data of environmental metadata, species occurrence and images; (2) Develop a community-based citizen-science network (fishers, researchers, industry partners, students, etc.) to gather new data (images, metadata and environmental DNA (eDNA)), train on and test the portable eDNA kits and SeaTraceBlueNet prototype to build the collaborative capacity to establish a standardized traceability system; and, (3) Set a system in place to connect traceability, sustainability and legality to support the development of a blue economy around the octopus value chain, incorporating the best practices and existing standards from stakeholders. This project is forward-thinking in drawing upon the perspective, ideas, expertise, and skillsets of the team members that represent a diversity of backgrounds, races, ethnicities, ages, and geographic regions. Over half of the team of co-PIs and Senior Personnel are women and/or persons of color. Most of the research team and industry partners are geographically located within the United States, yet this project is further strengthened by experts based in both developed and emerging nations as seafood traceability requires a global solution. Broadly, this project promotes coordinated use of multiple new and existing fisheries knowledge and data for transformative, accurate monitoring of key marine bioresources.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Overview
In 2021, Loyola Marymount University received a $750,000 Convergence Accelerator award entitled “NSF Convergence Accelerator Track E: Innovative Seafood Traceability Network for Sustainable Use, Improved Market Access, and Enhanced Blue Economy”. The project was commonly referred to as Seafood Tracer. Grounded in use-inspired research and leveraging human-centered design principles, the project sought to develop a cross-cutting traceability application to accelerate accurate and inclusive monitoring and management of targeted fisheries species entering the American marketplace.

 

The Problem
The ocean represents an important part of the solution to humanity’s challenges ranging from food and nutrition security to social and economic development, to environmental protection. To halt past failures of overfishing and other ecological damages, major fisheries require reimagined monitoring and management. Fishery resources need species-specific action, but real-time quantification currently is not possible. Compounding this is the reality that species and their ranges tend to be ill-defined. A fishery currently undergoing accelerating misuse is cephalopods (i.e., octopus and squid), with octopus fisheries in particularly dire straits due to their exploitation as a high-potential bioresource given nutritional value and culinary attractiveness.

Challenges facing octopus fisheries, like many other harvested species, include a dearth of data on recruitment and a lack of infrastructure within the fishery. Robust traceability methods promise the most effective tool for supporting management throughout the cephalopod supply chain. To facilitate broad adoption, seafood traceability methods must be easily replicable and affordable for fisheries management and to combat illegal fishing. Furthermore, transparency at each transaction from seafood producer to consumer will create trust and support for regulations, and, ultimately, decisions towards greater sustainability.

Outcomes
Informed by over 30 customer discovery interviews and dozens of meetings with seafood stakeholders from industry, government, NGOs, and academia, the Seafood Tracer team designed a prototype traceability solution that utilized passively collected environmental DNA (eDNA) from the wastewater stream within a seafood processing plant and from storage containers at active fishing ports to identify presence species. The resulting data was then contrasted to those species listed on invoices and manifest reports by the seafood company or fisher to identify discrepancies. The prototype is portable, fits within two suitcases, and is battery powered. At the end of this grant period, our team reached a processing time of ~12 hours to go from environmental sample to obtain first reads from the sequencer.

Furthermore, we built an online tool that used computer vision artificial intelligence to identify octopus photos to species level. This tool was designed to function independently or as a ground-truthing redundancy to the eDNA prototype tool for higher confidence and accuracy in species identification. The built machine learning model was trained on ~6,000 photographs and validated by taxonomy experts on our team.  

Over the 12 month duration of this project, members of the Seafood Tracer team presented at least eight conference presentations, published two scientific manuscripts, completed and published complete genomes for four commercially important octopus species, and secured one provisional U.S. Patent (full patent pending at time of this report). This project brought together an international team of 20 scientists and experts, including four undergraduate students, from a broad range of fields including biology, computer science,

Although this project was not selected for Phase II funding to scale up and implement more broadly, many deliverables and outcomes from this work are published and made available for continued development and potential use. Parties interested in these deliverables and outcomes are encouraged to contact the project’s PI and co-PIs.

Summary
Seafood Tracer applied convergence research concepts to lay a foundation in solving the concurrent global challenges of food security, fisheries sustainability, and transparency in seafood trade. Seafood Tracer’s completed prototype tool sought to create scalable, affordable technology to accelerate transformative monitoring of key marine fisheries and throughout the seafood supply chain.

 


Last Modified: 12/20/2023
Modified by: Demian A Willette

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