Abstract

Sea cucumber populations around the globe are experiencing marked declines caused by overexploitation and habitat degradation. Fisheries-independent data used to manage these ecologically and economically important species are frequently collected using diver- or snorkeler-based surveys, which have a number of limitations, including small spatial coverage and observer biases. In the present study, we explored how pairing traditional transect surveys with unmanned aerial vehicles (UAVs) and machine learning could improve sea cucumber density estimation in shallow environments. In July 2018, we conducted 24 simultaneous snorkeler–UAV transects in Tetiaroa, French Polynesia. All UAV images were independently reviewed by three observers and a convolution neural network (CNN) model: ResNet50. All three methods (snorkelers, manual review of UAV images, and ResNet50) produced similar counts, except at relatively high densities (∼75 sea cucumber 40 m⁻²), where UAVs and CNNs began to underestimate. Using a UAV-derived photomosaic of the study site, we simulated potential transect locations and determined a minimum of five samples were required to reliably estimate densities, while sample variance plateaued after 25 transects. Collectively, these results illustrate UAVs’ ability to survey small invertebrate species, while saving time, money, and labour compared to traditional methods, and highlights their potential to maximize efficiency when designing transect surveys.

中文翻译：

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使用无人机和机器学习来改善浅海生境中的海参密度估计

摘要

由于过度开发和栖息地退化，全球海参种群正在显着减少。经常使用基于潜水员或浮潜者的调查来收集用于管理这些具有生态和经济意义的物种的不依赖渔业的数据，这些调查有很多限制，包括较小的空间覆盖范围和观察者的偏见。在本研究中，我们探索了将传统样面调查与无人飞行器（UAV）和机器学习配合使用如何在浅水环境中改善海参密度估计的方法。2018年7月，我们在法属波利尼西亚的Tetiaroa同时进行了24个浮潜–UAV横断面。所有的无人机图像均由三名观察员和卷积神经网络（CNN）模型ResNet50独立审查。所有三种方法（浮潜，手动检查无人机图像，^-2），无人机和CNN开始被低估。使用无人机衍生的研究地点的光马赛克，我们模拟了潜在的样点位置，并确定了至少需要五个样本才能可靠地估计密度，而样点在25个样点后趋于平稳。总的来说，这些结果说明了无人机调查小型无脊椎动物物种的能力，同时与传统方法相比节省了时间，金钱和劳力，并突出了其在设计横断面调查时最大程度提高效率的潜力。