Scripps Institution of Oceanography, La Jolla, CA, USA.
Protected Resources and Biodiversity Division, NOAA NMFS Southeast Fisheries Science Center, Miami, FL, USA.
Sci Rep. 2021 Apr 15;11(1):8240. doi: 10.1038/s41598-021-87577-1.
Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier's beaked whales, sperm whales, and Risso's dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.
为了了解鲸目动物出现的时空模式并减轻人为影响，需要使用分布模型。船载线截视调查是估计丰度和描述鲸目动物种群分布的标准方法。船载调查提供了高空间分辨率，但缺乏时间分辨率和季节性覆盖。固定被动声学监测 (PAM) 使用声学传感器几乎连续地对点位置进行采样，在当地栖息地提供了高时间分辨率，跨越了数天、数季和数年。为了评估跨平台数据综合是否可以改善分布预测，使用两种不同的方法：广义加性模型和神经网络，为墨西哥湾的 Cuvier 喙鲸、抹香鲸和里氏海豚开发了模型。神经网络能够学习驾驶员之间未指定的相互作用。纳入 PAM 数据集的模型表现优于仅基于视觉数据训练的模型，在三种情况下有两种模型表现最佳。建模结果表明，当结合使用时，使用多种数据类型的多个物种分布模型可以通过提高对时间和空间物种分布趋势的理解，支持墨西哥湾鲸目动物种群的保护和管理。
