Global Change Institute, The University of Queensland, St Lucia, QLD, 4072, Australia.
School of Biological Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia.
Sci Data. 2020 Oct 20;7(1):355. doi: 10.1038/s41597-020-00698-6.
Addressing the global decline of coral reefs requires effective actions from managers, policymakers and society as a whole. Coral reef scientists are therefore challenged with the task of providing prompt and relevant inputs for science-based decision-making. Here, we provide a baseline dataset, covering 1300 km of tropical coral reef habitats globally, and comprised of over one million geo-referenced, high-resolution photo-quadrats analysed using artificial intelligence to automatically estimate the proportional cover of benthic components. The dataset contains information on five major reef regions, and spans 2012-2018, including surveys before and after the 2016 global bleaching event. The taxonomic resolution attained by image analysis, as well as the spatially explicit nature of the images, allow for multi-scale spatial analyses, temporal assessments (decline and recovery), and serve for supporting image recognition developments. This standardised dataset across broad geographies offers a significant contribution towards a sound baseline for advancing our understanding of coral reef ecology and thereby taking collective and informed actions to mitigate catastrophic losses in coral reefs worldwide.
解决全球珊瑚礁减少的问题需要管理者、政策制定者和整个社会采取有效行动。因此,珊瑚礁科学家面临着为基于科学的决策提供及时和相关投入的任务。在这里,我们提供了一个基线数据集,涵盖全球热带珊瑚礁栖息地的 1300 公里,由超过一百万个经过地理参考的、使用人工智能分析的高分辨率照片四方形组成,用于自动估计底栖成分的比例覆盖。该数据集包含五个主要珊瑚礁区域的信息,时间跨度为 2012 年至 2018 年,包括 2016 年全球白化事件之前和之后的调查。图像分析达到的分类分辨率以及图像的空间显式性质,允许进行多尺度空间分析、时间评估(下降和恢复),并支持图像识别的发展。这个跨广泛地理区域的标准化数据集为推进我们对珊瑚礁生态学的理解提供了重要贡献,从而采取集体和知情的行动,减轻全球珊瑚礁的灾难性损失。