The eastern oyster (Crassostrea virginica) is a coastal foundation species currently under threat from anthropogenic activities both globally and in the Apalachicola Bay region of north Florida. Oysters provide numerous ecosystem services, and it is important to establish efficient and reliable methods for their effective monitoring and management. Traditional monitoring techniques, such as quadrat density sampling, can be labor‐intensive, destructive of both oysters and reefs, and may be spatially limited. In this study, we demonstrate how unoccupied aerial systems (UAS) can be used to efficiently generate high‐resolution geospatial oyster reef condition data over large areas. These data, with appropriate ground truthing and minimal destructive sampling, can be used to effectively monitor the size and abundance of oyster clusters on intertidal reefs. Utilizing structure‐from‐motion photogrammetry techniques to create three‐dimensional topographic models, we reconstructed the distribution, spatial density and size of oyster clusters on intertidal reefs in Apalachicola Bay. Ground truthing revealed 97% accuracy for cluster presence detection by UAS products and we confirmed that live oysters are predominately located within clusters, supporting the use of cluster features to estimate oyster population status. We found a positive significant relationship between cluster size and live oyster counts. These findings allowed us to extract clusters from geospatial products and predict live oyster abundance and spatial density on 138 reefs covering 138 382 m2 over two locations. Oyster densities varied between sites, with higher live oyster densities occurring at one site within the Apalachicola Bay bounds, and lower oyster densities in areas adjacent to Apalachicola Bay. Repeated monitoring at one site in 2022 and 2023 revealed a relatively stable oyster density over time. This study demonstrated the successful application of high‐resolution drone imagery combined with cluster sampling, providing a repeatable method for mapping and monitoring to inform conservation, restoration and management strategies for intertidal oyster populations.

中文翻译：

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应用无人航空系统（UAS）监测潮间带牡蛎密度和丰度


东部牡蛎（弗吉尼亚牡蛎）是一种沿海基础物种，目前在全球范围内以及佛罗里达州北部的阿巴拉契科拉湾地区都受到人类活动的威胁。牡蛎提供众多的生态系统服务，建立高效可靠的方法对其进行有效监测和管理非常重要。传统的监测技术，例如样方密度采样，可能是劳动密集型的，对牡蛎和珊瑚礁都有破坏性，而且可能受到空间限制。在这项研究中，我们展示了如何使用无人航空系统（UAS）有效地生成大面积的高分辨率地理空间牡蛎礁状况数据。这些数据加上适当的地面实况和最小破坏性采样，可用于有效监测潮间带珊瑚礁上牡蛎簇的大小和丰度。利用运动结构摄影测量技术创建三维地形模型，我们重建了阿巴拉契科拉湾潮间礁石上牡蛎簇的分布、空间密度和大小。地面实况显示 UAS 产品对集群存在检测的准确度为 97%，我们确认活牡蛎主要位于集群内，支持使用集群特征来估计牡蛎种群状态。我们发现簇大小和活牡蛎数量之间存在显着正相关关系。这些发现使我们能够从地理空间产品中提取聚类，并预测覆盖 138 382 m 的 138 个珊瑚礁上的活牡蛎丰度和空间密度2超过两个地点。 牡蛎密度因地点而异，阿巴拉契科拉湾范围内的某一地点活牡蛎密度较高，而阿巴拉契科拉湾附近地区的牡蛎密度较低。 2022 年和 2023 年对一个地点的重复监测显示，随着时间的推移，牡蛎密度相对稳定。这项研究证明了高分辨率无人机图像与集群采样相结合的成功应用，提供了一种可重复的测绘和监测方法，为潮间带牡蛎种群的保护、恢复和管理策略提供信息。