Computing ecosystem risk hotspots: A mediterranean case study
Created March 19, 2026
Updated on March 25, 2026
approved
Tags:
Basic
Language
English
MainTitle
Computing ecosystem risk hotspots: A mediterranean case study
Original ids
10.1016/j.ecoinf.2024.102918; 20.500.14243/516017
Type
publication
bestAccessRight
OPEN
countries
Italy
Creator/Author
Full name
Coro G., orcid: 0000-0001-7232-191x ; Pavirani L., orcid: ; Ellenbroek A., orcid:
Other
Description
In ecosystem management, risk assessment quantifies the probability and impact of events and informs on intervention priorities. Analytical models for risk assessment quantify the impact of natural and anthropogenic stressors on ecosystems. Traditional approaches evaluate single stressors, whereas complex models assess cumulative impacts of frequently interacting stressors and offer better accuracy at the expense of low cross-area re-applicability and long implementation times.We introduce a versatile, re-useable, and semi-automated workflow designed for big data-driven ecosystem risk assessment, utilising spatiotemporal data from open repositories. It allows for a flexible definition of the stressors on which the risk under analysis depends. By applying cluster analysis, the workflow identifies different patterns of stressor concurrency, while statistical analysis highlights clusters of stressors likely linked to elevated risk. Ultimately, it generates geospatial risk maps and identifies spatial risk hotspots. The workflow methodology is independent of the geographical area of the application.As a case study, we present risk assessments for the Mediterranean Sea, a region with intense anthropogenic pressures and significant climatic vulnerabilities. We used over 1.1 million open data from 2017 to 2021 and projections to 2050 under the RCP8.5 scenario (a high greenhouse gas emission scenario) at a 0.5°spatial resolution. Data included environmental, oceanographic, biodiversity variables, and manifest and hidden fishing effort distributions. Our workflow identified different types of high-risk hotspots, highlighting different concurrencies of habitat loss, overfishing, hidden fishing, and climate change stressors. High-risk hotspots concentrated in the Western Mediterranean, the Tyrrhenian Sea, the Adriatic Sea, the Strait of Sicily, the Aegean Sea, and eastern Turkey. Our results agreed with an alternative Fuzzy C-means-based method (with a 90% to 96% overlap over t
Publication Date
2025-03-01
Publisher
Elsevier BV
Subjects
Ecosystem vulnerability; Risk assessment, Ecosystem vulnerability, Mediterranean sea, Data mining, Cluster analysis, Open science; Cluster analysis; Ecology; Mediterranean sea; Open science; Information technology; T58.5-58.64; Data mining; QH540-549.5; Risk assessment
isGreen
true
isInDiamondJournal
false
Publication
Name
Ecological Informatics
Publication
Article
Starting page
102918
issnPrinted
1574-9541
vol
85
Other Research Product
Detailed informations
system:type
Research Product
Management Info
Author
Last Updated
March 25, 2026, 10:49 (UTC)
Created
March 19, 2026, 00:32 (UTC)
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