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Wind power is one of the fastest growing renewable energy technologies. Environmental impact assessment is crucial for the sustainable development of offshore wind farms (OWF). The aim of the current study is to identify vulnerable areas for supporting OWF planning around Pantelleria Island. Seven species of high-conservation interest were selected based on species groups listed within results of Descriptor 1 of the Marine Strategy Framework Directive 2008/56/EC, and occurrence data were sourced from the Global Biodiversity Information Facility. Nine environmental variables were analyzed using Ecological Niche Models (ENMs) to estimate habitat suitability. Four modeling algorithms (Generalized Linear Model, Random Forest, MaxEnt, and Support Vector Machine) were employed to create habitat suitability maps, which were then combined into a vulnerability map. Results showed that the models performed well, with AUC scores ranging from 0.86 to 0.99. The vulnerability map highlighted areas of high species occurrence, aiding in the identification of marine areas suitable for OWF siting while minimizing biodiversity impacts.

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Assessing Habitat Suitability and Vulnerability for Marine Species in Offshore Wind Farm Planning: A Case Study of Pantelleria Island

  • 会议论文

  • 发表日期：

    2025年

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  • Giovanna Cilluffo,
  • Gianluca Sottile,
  • Laura Ciriminna,
  • Geraldina Signa,
  • Agostino Tomasello,
  • Salvatrice Vizzini

摘要

Wind power is one of the fastest growing renewable energy technologies. Environmental impact assessment is crucial for the sustainable development of offshore wind farms (OWF). The aim of the current study is to identify vulnerable areas for supporting OWF planning around Pantelleria Island. Seven species of high-conservation interest were selected based on species groups listed within results of Descriptor 1 of the Marine Strategy Framework Directive 2008/56/EC, and occurrence data were sourced from the Global Biodiversity Information Facility. Nine environmental variables were analyzed using Ecological Niche Models (ENMs) to estimate habitat suitability. Four modeling algorithms (Generalized Linear Model, Random Forest, MaxEnt, and Support Vector Machine) were employed to create habitat suitability maps, which were then combined into a vulnerability map. Results showed that the models performed well, with AUC scores ranging from 0.86 to 0.99. The vulnerability map highlighted areas of high species occurrence, aiding in the identification of marine areas suitable for OWF siting while minimizing biodiversity impacts.