Computing ecosystem risk hotspots: A mediterranean case study
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 c...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004606 |
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| author | Gianpaolo Coro Laura Pavirani Anton Ellenbroek |
| author_facet | Gianpaolo Coro Laura Pavirani Anton Ellenbroek |
| author_sort | Gianpaolo Coro |
| collection | DOAJ |
| 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 the years) and a Bayesian regression model (∼80% overlap).Our Mediterranean risk maps can facilitate the development of management and monitoring strategies, supporting the sustainable development and resilience of coastal zones, and can act as prior knowledge for ecosystem models and spatial plans. |
| format | Article |
| id | doaj-art-5bb68eef17a64d65ab5b41607c260df5 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-5bb68eef17a64d65ab5b41607c260df52025-01-19T06:24:33ZengElsevierEcological Informatics1574-95412025-03-0185102918Computing ecosystem risk hotspots: A mediterranean case studyGianpaolo Coro0Laura Pavirani1Anton Ellenbroek2Institute of Information Science and Technologies “A. Faedo” of the National Research Council of Italy (ISTI-CNR), Via Moruzzi 1, Pisa, 56124, Italy; Corresponding author.Institute of Information Science and Technologies “A. Faedo” of the National Research Council of Italy (ISTI-CNR), Via Moruzzi 1, Pisa, 56124, Italy; Institute of Marine Environmental Research of the National Research Council of Italy (ISMAR-CNR), Pozzuolo di via Santa Teresa, Lerici, 19032, ItalyFood and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, 50, Rome, 00153, ItalyIn 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 the years) and a Bayesian regression model (∼80% overlap).Our Mediterranean risk maps can facilitate the development of management and monitoring strategies, supporting the sustainable development and resilience of coastal zones, and can act as prior knowledge for ecosystem models and spatial plans.http://www.sciencedirect.com/science/article/pii/S1574954124004606Risk assessmentEcosystem vulnerabilityMediterranean seaData miningCluster analysisOpen science |
| spellingShingle | Gianpaolo Coro Laura Pavirani Anton Ellenbroek Computing ecosystem risk hotspots: A mediterranean case study Ecological Informatics Risk assessment Ecosystem vulnerability Mediterranean sea Data mining Cluster analysis Open science |
| title | Computing ecosystem risk hotspots: A mediterranean case study |
| title_full | Computing ecosystem risk hotspots: A mediterranean case study |
| title_fullStr | Computing ecosystem risk hotspots: A mediterranean case study |
| title_full_unstemmed | Computing ecosystem risk hotspots: A mediterranean case study |
| title_short | Computing ecosystem risk hotspots: A mediterranean case study |
| title_sort | computing ecosystem risk hotspots a mediterranean case study |
| topic | Risk assessment Ecosystem vulnerability Mediterranean sea Data mining Cluster analysis Open science |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004606 |
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