Geology topics

Ioannis Athanasiadis

Publications and source records attributed to Ioannis Athanasiadis.

3 recordsLinked to original sources

USGS2025

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

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.

Ecosystem Services↗

USGS2019

Towards globally customizable ecosystem service models

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.

Science of the Total Environment↗

USGS2018

Machine learning for ecosystem services

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.

Ecosystem Services↗