TY - JOUR

T1 - Interoperability for ecosystem service assessments: Why, how, who, and for whom?

AU - Bagstad, Kenneth

AU - Balbi, Stefano

AU - Adamo, Greta

AU - Athanasiadis, Ioannis N.

AU - Affinito, Flavio

AU - Willcock, Simon

AU - Magrach, Ainhoa

AU - Hayashi, Kiichiro

AU - Harmackova, Zuzana V.

AU - Niamir, Aidin

AU - Smets, Bruno

AU - Buchhorn, Marcel

AU - Drakou, Evangelia G.

AU - Alfieri, Alessandra

AU - Edens, Bram

AU - Morales, Luis Gonzalez

AU - Vardi, Agnes

AU - Sanz, Maria-Jose

AU - Villa, Ferdinando

PY - 2025/3/4

Y1 - 2025/3/4

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Ecosystem service monitoring

KW - FAIR

KW - Interoperability

KW - Knowledge reuse

KW - Semantics

U2 - 10.1016/j.ecoser.2025.101705

DO - 10.1016/j.ecoser.2025.101705

M3 - Article

JO - Ecosystem Services

JF - Ecosystem Services

SN - 2212-0416

M1 - 101705

ER -