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Characterizing the benthic community in Maryland’s offshore wind energy areas using a towed camera sled: Developing a method to reduce the effort of image analysis and community description

机译:使用拖曳的摄像头雪橇表征马里兰州海上风能区的底栖生物群落:开发一种减少图像分析和群落描述工作的方法

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摘要

Offshore wind farms are a crucial component for the improvement of renewable energy in the United States. The Bureau of Ocean Energy Management (BOEM) designated ~170 km2 of shelf area for wind energy development off the coast of Maryland, USA. In order to understand potential environmental impacts of wind turbine installation on the benthic ecosystem within the designated area, we conducted a study to visually characterize bottom habitats and epibenthic communities in the Mid-Atlantic Outer Continental Shelf blocks of the Maryland wind energy area. Seven 5 km long transects were sampled using a towed camera sled with a downward-facing digital camera that captured images at 5 frames·s-1s. Additional small-mesh beam trawling was also conducted at selected locations complementary for species identification. Image data were analyzed using two image selection methods, random and systematic (i.e. video frames were selected at various intervals). For both methods, estimates of community diversity (Hill’s N2) stabilized with sample sizes ranging from 316 to 398 frames. Our results allowed us to define distinct epibenthic communities and bottom habitats that are associated with offshore wind energy sites and to develop a sampling technique for digital images that can be applied to other research programs.
机译:海上风电场是改善美国可再生能源的重要组成部分。海洋能源管理局(BOEM)指定了约170 km 2 架子区,用于美国马里兰州沿海的风能开发。为了了解风力涡轮机安装对指定区域底栖生态系统的潜在环境影响,我们进行了一项研究,以视觉方式表征马里兰州风能区域的中大西洋外围大陆架块的底部栖息地和表生动物群落。使用拖曳式相机底座和向下的数码相机对七个5公里长的样面进行采样,该相机以5帧·s -1 s的速度捕获图像。还在与物种识别互补的选定位置进行了小网孔拖网作业。使用随机和系统两种图像选择方法分析图像数据(即以不同间隔选择视频帧)。对于这两种方法,社区多样性的估计值(Hill的N2)均稳定在316至398帧的样本量范围内。我们的结果使我们能够定义与海上风能站点相关的独特的附生动物群落和海底栖息地,并开发出可用于其他研究计划的数字图像采样技术。

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