Data underlying the publication: Offshore wind farms contribute to epibenthic biodiversity in the North Sea.

Video footage of epibenthic organisms at and around the scour protection in offshore wind farms was collected using a Remotely Operated Vehicle. The epibenthic community structure was assessed for species abundance and species diversity (species richness (<em>S</em>), species evenness (<em>E</em>) and Shannon diversity index (<em>H</em>)). Species density for individual species were calculated as the number of individuals per m2 in a video frame; species density of clustering species was calculated in percentage as covered area per video frame. These different types of densities were combined by transforming them to the ordinal Marine Nature Conservation Review (MNCR) SACFOR scale. Statistical analyses were performed using the software package R version 3.6.3 with several functions from the ‘vegan package’. Before statistical analyses, species with only 1 observation in the dataset were removed to minimize the influence of rare species in multivariate analyses. To obtain a balanced dataset, the Monte Carlo resampling strategy was applied (by 100 randomized repetitions). <br> For further information see manuscript.

本数据集通过遥控水下机器人（Remotely Operated Vehicle, ROV）采集了海上风电场冲刷防护区域及其周边的底上表栖生物（epibenthic organisms）视频影像。针对底上表栖群落结构，研究人员对物种丰度及物种多样性（包括物种丰富度<em>S</em>、物种均匀度<em>E</em>和香农多样性指数（Shannon diversity index, <em>H</em>））开展了评估。单物种的物种密度以单帧视频内每平方米的个体数计算；集群物种的物种密度则以单帧视频内的覆盖面积百分比计算。将上述不同类型的密度数据转换为有序的海洋自然保护审查（Marine Nature Conservation Review, MNCR）SACFOR等级尺度后进行统一整合。统计分析采用R软件版本3.6.3及vegan包中的多个函数完成。开展统计分析前，本研究移除了数据集中仅出现1次的物种，以最小化稀有种对多元统计分析的影响。为获得均衡数据集，本研究采用蒙特卡洛重采样策略，共执行100次随机重复抽样。
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