Interoperability for ecosystem service assessments: Why, how, who, and for whom?

Kenneth Bagstad, Stefano Balbi, Greta Adamo, Ioannis N. Athanasiadis, Flavio Affinito, Simon Willcock, Ainhoa Magrach, Kiichiro Hayashi, Zuzana V. Harmackova, Aidin Niamir, Bruno Smets, Marcel Buchhorn, Evangelia G. Drakou, Alessandra Alfieri, Bram Edens, Luis Gonzalez Morales, Agnes Vardi, Maria-Jose Sanz, Ferdinando Villa

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Abstract

Despite continued, rapid growth in the literature, the fragmentation of information is a major barrier to more timely and credible ecosystem services (ES) assessments. A major reason for this fragmentation is the currently limited state of interoperability of ES data, models, and software. The FAIR Principles, a recent reformulation of long-standing open science goals, highlight the importance of making scientific knowledge Findable, Accessible, Interoperable, and Reusable. Critically, FAIR aims to make science more transparent and transferable by both people and computers. However, it is easier to make data and models findable and accessible through data and code repositories than to achieve interoperability and reusability. Achieving interoperability will require more consistent adherence to current technical best practices and, more critically, to build consensus about and consistently use semantics that can represent ES-relevant phenomena. Building on recent examples from major international initiatives for ES (IPBES, SEEA, GEO BON), we illustrate strategies to address interoperability, discuss their importance, and describe potential gains for individual researchers and practitioners and the field of ES. Although interoperability comes with many challenges, including greater scientific coordination than today’s status quo, it is technically achievable and offers potentially transformative advantages to ES assessments needed to mainstream their use by decision makers. Individuals and organizations active in ES research and practice can play critical roles in creating widespread interoperability and reusability of ES science. A representative community of practice targeting interoperability for ES would help advance these goals.
Original languageEnglish
Article number101705
JournalEcosystem Services
Volume72
Early online date4 Mar 2025
DOIs
Publication statusPublished - 1 Apr 2025

Keywords

  • Artificial Intelligence
  • Ecosystem service monitoring
  • FAIR
  • Interoperability
  • Knowledge reuse
  • Semantics

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