Wind Energy, Its Application, Challenges, and Potential Environmental Impact

Nazir, Muhammad Shahzad; Wang, Yeqin; Bilal, Muhammad; Abdalla, Ahmad N.

doi:10.1007/978-1-4614-6431-0_108-1

Cited by 1 publication

(1 citation statement)

References 54 publications

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“…However, our current understanding of marine autecology in habitats allocated for offshore wind exploitation is largely limited and remains unresolved for many species of concern (e.g., Drewitt and Langston, 2006;Bergstrom et al, 2013;Bailey et al, 2014;Bergstrom et al, 2014). Critical data-gaps in the literature regarding the potential for spatiotemporal overlap between species movements distributions and offshore renewable activities are only further compounded by the difficulty of monitoring cryptic or highly migratory populations across broad, spatially heterogeneous marine areas-increasing the potential for regulatory incongruities as offshore development timelines outpace the ability of resource managers to identify appropriate conservation strategies (Boehlert and Gill, 2010;Nazir et al, 2021) In the United States, aggressive clean energy targets and favorable domestic policies have driven rapid growth in the offshore wind energy market (Sun et al, 2020;Alola and Akadiri, 2021).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and species distribution ensemble models inform resource interactions with offshore wind development

Ingram,

Dunton,

Frisk

et al. 2024

Preprint

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Development of offshore wind energy resources has led to growing concerns for marine wildlife. However, significant uncertainty remains regarding the technology’s potential to impact species of interest that may occupy planned development sites. This is further compounded by the difficulty of monitoring highly migratory or data-poor species in marine waters, making practical assessment of site- or species-specific threats that could require additional management intervention particularly problematic. Here, I identify a highly generalizable framework to inform species interactions in marine habitats allocated for offshore resource exploitation, using telemetry-derived artificial intelligence species distribution models. Results from a case study of the federally protected Atlantic Sturgeon (Acipenser oxyrinchus) demonstrate excellent discriminatory capacity (i.e., AUC ≥ 0.9) at a relatively fine scale (raster resolution = 1 km2), while providing critical information on predicted occurrence over a broad swath of unmonitored marine habitats (i.e., the Atlantic OCS region of the US; area > 620,000 km2). Furthermore, ensemble map products developed from these models are readily scalable to ongoing management needs and, when overlaid with offshore wind energy lease areas, can feed directly into management strategies to inform best practices for potential habitat influences on Atlantic Sturgeon, as well as other species of commercial or conservation interest.

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