Yesson Chris, Letessier Tom B, Nimmo-Smith Alex, Hosegood Phil, Brierley Andrew S, Hardouin Marie, Proud Roland
Institute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK.
School of Biological & Marine Science, University of Plymouth, Plymouth, Devon PL4 8AA, UK.
UCL Open Environ. 2021 Dec 22;3:e030. doi: 10.14324/111.444/ucloe.000030. eCollection 2021.
Seamounts are important marine habitats that are hotspots of species diversity. Relatively shallow peaks, increased productivity and offshore locations make seamounts vulnerable to human impact and difficult to protect. Present estimates of seamount numbers vary from anywhere between 10,000 to more than 60,000. Seamount locations can be estimated by extracting large, cone-like features from bathymetry grids (based on criteria of size and shape). These predicted seamounts are a useful reference for marine researchers and can help direct exploratory surveys. However, these predictions are dependent on the quality of the surveys underpinning the bathymetry. Historically, quality has been patchy, but is improving as mapping efforts step up towards the target of complete seabed coverage by 2030. This study presents an update of seamount predictions based on SRTM30 PLUS global bathymetry version 11 and examines a potential source of error in these predictions. This update was prompted by a seamount survey in the British Indian Ocean Territory in 2016, where locations of two putative seamounts were visited. These 'seamounts' were targeted based on previous predictions, but these features were not detected during echosounder surveys. An examination of UK hydrographic office navigational (Admiralty) charts for the area showed that the summits of these putative features had soundings reporting 'no bottom detected at this depth' where 'this depth' was similar to the seabed reported from the bathymetry grids: we suspect that these features likely resulted from an initial misreading of the charts. We show that 15 'phantom seamount' features, derived from a misinterpretation of no bottom sounding data, persist in current global bathymetry grids and updated seamount predictions. Overall, we predict 37,889 seamounts, an increase of 4437 from the previous predictions derived from an older global bathymetry grid (SRTM30 PLUS v6). This increase is due to greater detail in newer bathymetry grids as acoustic mapping of the seabed expands. The new seamount predictions are available at https://doi.pangaea.de/10.1594/PANGAEA.921688.
海山是重要的海洋栖息地，也是物种多样性的热点地区。相对较浅的山峰、较高的生产力以及离岸位置，使得海山容易受到人类活动的影响，且难以得到保护。目前对海山数量的估计在1万至6万多座之间。海山的位置可以通过从测深网格中提取大型圆锥状特征（基于大小和形状标准）来估算。这些预测的海山对海洋研究人员来说是一个有用的参考，有助于指导探索性调查。然而，这些预测依赖于支撑测深数据的调查质量。从历史上看，数据质量参差不齐，但随着测绘工作朝着到2030年实现海底全面覆盖的目标推进，质量正在提高。本研究基于SRTM30 PLUS全球测深版本11对海山预测进行了更新，并研究了这些预测中一个潜在的误差来源。此次更新是由2016年在英属印度洋领地进行的一次海山调查引发的，在该调查中，对两座假定海山的位置进行了探测。这些“海山”是根据之前的预测选定的目标，但在回声测深调查中并未检测到这些特征。对该地区英国水文局航海（海军部）海图的检查显示，这些假定特征的顶部测深报告为“在此深度未检测到海底”，而“此深度”与测深网格报告的海底深度相似：我们怀疑这些特征可能是最初对海图的误读所致。我们发现，由于对无海底测深数据的错误解读而产生的15个“幽灵海山”特征，在当前的全球测深网格和更新后的海山预测中依然存在。总体而言，我们预测有37889座海山，比之前基于旧的全球测深网格（SRTM30 PLUS v6）得出的预测增加了4437座。这一增加是由于随着海底声学测绘范围的扩大，新的测深网格更加详细。新的海山预测可在https://doi.pangaea.de/10.1594/PANGAEA.921688获取。
