Capacity development for technology adoption in fisheries and conservation requires two-way learning

  • Aimée F. Komugabe-Dixson
  • , Edaysi Bucio Bustos
  • , Alejandro Canio
  • , Carlos Chacon
  • , Rodrigo Claudino
  • , Nancy De Lemos
  • , Henry G. W. Dixson
  • , Rocío Joo
  • , Cian Luck
  • , Krizia Matthews
  • , Annie Mejaes
  • , Moníca Espinoza-Miralles
  • , Adel Heenan

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    Capacity development is pivotal to meeting global development goals, as demonstrated by its prominence in the Sustainable Development Goals and other international agreements. Yet, despite this growing recognition, challenges persist in translating capacity development principles into effective, sustainable practices, due in part, to a limited empirical base to inform theory and implementation. This paper contributes to addressing this gap by examining the experience of Global Fishing Watch, a technology-focused non-profit supporting the use of satellite-based technologies and big data to improve fisheries monitoring and management. Drawing on three case studies in Latin America—a national authority managing a distant-water fleet, a transboundary initiative managing a biodiversity-rich marine corridor, and a multi-stakeholder response to foreign fishing fleet activity—we analysed the practical challenges and successes of technology adoption in these diverse contexts using thematic network analysis. We found that individual and institutional learning, sustained and flexible resourcing, and trusted partnerships were key enablers of effective capacity development and technology uptake. We also highlight tensions between innovation-driven interventions, formal institutional systems, and the dynamic realities of local implementation. The lessons shared here extend beyond the fisheries sector, offering insights for broader capacity development initiatives amid rapid technological change.

    Original languageEnglish
    Article numberfsaf142
    Number of pages16
    JournalICES Journal of Marine Science
    Volume82
    Issue number10
    DOIs
    Publication statusPublished - Sept 2025

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 4 - Quality Education
      SDG 4 Quality Education
    2. SDG 8 - Decent Work and Economic Growth
      SDG 8 Decent Work and Economic Growth
    3. SDG 14 - Life Below Water
      SDG 14 Life Below Water
    4. SDG 17 - Partnerships for the Goals
      SDG 17 Partnerships for the Goals

    Keywords

    • AI
    • capacity building
    • capacity development
    • fisheries management
    • Latin America
    • machine learning
    • satellite-based technology
    • SDGs
    • sustainable fisheries
    • technology transfer

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