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2025
Journal article
Open Access
Computing ecosystem risk hotspots: a mediterranean case study
Coro G., Pavirani L., Ellenbroek A.
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.Source: ECOLOGICAL INFORMATICS, vol. 85
DOI: 10.1016/j.ecoinf.2024.102918
Project(s): EcoScope
Metrics:
Coro G., Pavirani L., Ellenbroek A.
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.Source: ECOLOGICAL INFORMATICS, vol. 85
DOI: 10.1016/j.ecoinf.2024.102918
Project(s): EcoScope
Metrics:
See at:
Ecological Informatics 
| CNR IRIS | www.sciencedirect.com | CNR IRIS 
2025
Journal article
Open Access
Assessing marine ecosystem risks through unsupervised methods
Laura Pavirani, Pasquale Bove, Gianpaolo Coro
Marine ecosystems are facing significant challenges from intensified fishing, pollution, climate change, and biodiversity loss. Ecosystem risk assessments are vital for informing effective policies and management decisions. Traditional approaches, such as Ecosystem Models (EMs) and Marine Spatial Planning (MSP), often rely on expert knowledge, which introduces subjective assumptions. This study evaluates six unsupervised methods — four clustering algorithms (Multi K-means, Fuzzy C-means, X-means, and DBSCAN) and two machine-learning models (an Artificial Neural Network, ANN, and a Variational Autoencoder, VAE) — to assess marine ecosystem risk in the Mediterranean Sea automatically, using open-access data from 2017 to 2021. Each method generated five annual high-risk maps based on ecosystem variables, including fishing effort, species richness, depth, coastal proximity, oxygen levels, net primary production, and thermohaline circulation intensity. Our quantitative analysis of 30 generated maps revealed pairwise similarities ranging from 72.2% to 95.9%, with Cohen’s Kappa scores between 0.46 (moderate) and 0.91 (almost perfect). All methods consistently identified high-risk hotspots in the Eastern and Western Mediterranean, the Tyrrhenian Sea, the Adriatic Sea, the Strait of Sicily, and the Aegean Sea. However, we also found discrepancies due to the different tendencies of the models to produce broader (precautionary) or more focused (conservative) risk assessments. Assessments by DBSCAN, ANN, and VAE were similar (∼90%) and broader, whereas X-means was more conservative. Multi K-means and Fuzzy C-means exhibited similar (∼92%) and more balanced results. These findings provide a data-driven foundation and practical guidance for developing Bayesian EMs and MSP with reduced reliance on subjective assessments.Source: ECOLOGICAL INFORMATICS, vol. 90 (issue December), pp. 1-16
DOI: 10.1016/j.ecoinf.2025.103334
Project(s): EcoScope
Metrics:
Laura Pavirani, Pasquale Bove, Gianpaolo Coro
Marine ecosystems are facing significant challenges from intensified fishing, pollution, climate change, and biodiversity loss. Ecosystem risk assessments are vital for informing effective policies and management decisions. Traditional approaches, such as Ecosystem Models (EMs) and Marine Spatial Planning (MSP), often rely on expert knowledge, which introduces subjective assumptions. This study evaluates six unsupervised methods — four clustering algorithms (Multi K-means, Fuzzy C-means, X-means, and DBSCAN) and two machine-learning models (an Artificial Neural Network, ANN, and a Variational Autoencoder, VAE) — to assess marine ecosystem risk in the Mediterranean Sea automatically, using open-access data from 2017 to 2021. Each method generated five annual high-risk maps based on ecosystem variables, including fishing effort, species richness, depth, coastal proximity, oxygen levels, net primary production, and thermohaline circulation intensity. Our quantitative analysis of 30 generated maps revealed pairwise similarities ranging from 72.2% to 95.9%, with Cohen’s Kappa scores between 0.46 (moderate) and 0.91 (almost perfect). All methods consistently identified high-risk hotspots in the Eastern and Western Mediterranean, the Tyrrhenian Sea, the Adriatic Sea, the Strait of Sicily, and the Aegean Sea. However, we also found discrepancies due to the different tendencies of the models to produce broader (precautionary) or more focused (conservative) risk assessments. Assessments by DBSCAN, ANN, and VAE were similar (∼90%) and broader, whereas X-means was more conservative. Multi K-means and Fuzzy C-means exhibited similar (∼92%) and more balanced results. These findings provide a data-driven foundation and practical guidance for developing Bayesian EMs and MSP with reduced reliance on subjective assessments.Source: ECOLOGICAL INFORMATICS, vol. 90 (issue December), pp. 1-16
DOI: 10.1016/j.ecoinf.2025.103334
Project(s): EcoScope
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2025
Conference article
Restricted
Ecosystem risk Aassessment through stressor concurrency identification: a comparative analysis
Laura Pavirani, Pasquale Bove, Gianpaolo Coro
In Marine Science, ecosystem risk assessment is a process that integrates data to estimate the potential impact of harmful and fragile forces (stressors) on the ecosystem. Nowadays, the management of marine ecosystems is increasingly complex due to multiple stressors, including human activities and climate change, and requires robust tools to address challenges effectively. We present big data-driven methods that enable a rapid, simultaneous analysis of multiple stressors using unsupervised learning techniques and statistical analysis to produce prior ecosystem risk assessments. We apply four cluster analysis methods based on Multi K-means, Fuzzy C-means, X-means, and DBSCAN, to identify stressor concurrency areas in Mediterranean Sea data from 2017 to 2021. These data include stressor variables related to environmental, oceanographic, fishing, and biodiversity factors. The methods assess ecosystem risk by detecting high stressor concurrency conditions. Finally, they produce maps that highlight potential high-risk regions. We compare the results of the four methods to examine the similarities and differences in their abilities to detect high-risk areas. From the Mediterranean data, all methods jointly indicate known high-risk areas but differ in the extent of the identified areas. Our comparative analysis highlights the importance of selecting the most appropriate clustering technique based on the balance between precautionary (highlighting broader areas) and conservative (highlighting smaller areas) perspectives. The results provide information that should be used in ecosystem models and marine spatial planning to improve the accuracy and objectivity of ecosystem risk assessment and management strategies.DOI: 10.1109/oceans58557.2025.11104682
Project(s): EcoScope, ITINERIS PNRR Italian project
Metrics:
Laura Pavirani, Pasquale Bove, Gianpaolo Coro
In Marine Science, ecosystem risk assessment is a process that integrates data to estimate the potential impact of harmful and fragile forces (stressors) on the ecosystem. Nowadays, the management of marine ecosystems is increasingly complex due to multiple stressors, including human activities and climate change, and requires robust tools to address challenges effectively. We present big data-driven methods that enable a rapid, simultaneous analysis of multiple stressors using unsupervised learning techniques and statistical analysis to produce prior ecosystem risk assessments. We apply four cluster analysis methods based on Multi K-means, Fuzzy C-means, X-means, and DBSCAN, to identify stressor concurrency areas in Mediterranean Sea data from 2017 to 2021. These data include stressor variables related to environmental, oceanographic, fishing, and biodiversity factors. The methods assess ecosystem risk by detecting high stressor concurrency conditions. Finally, they produce maps that highlight potential high-risk regions. We compare the results of the four methods to examine the similarities and differences in their abilities to detect high-risk areas. From the Mediterranean data, all methods jointly indicate known high-risk areas but differ in the extent of the identified areas. Our comparative analysis highlights the importance of selecting the most appropriate clustering technique based on the balance between precautionary (highlighting broader areas) and conservative (highlighting smaller areas) perspectives. The results provide information that should be used in ecosystem models and marine spatial planning to improve the accuracy and objectivity of ecosystem risk assessment and management strategies.DOI: 10.1109/oceans58557.2025.11104682
Project(s): EcoScope
, ITINERIS PNRR Italian projectMetrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS