Predictive GAM seabed maps can account for defined and fuzzy boundaries to improve accuracy in a scottish sea loch seascape

IF 2.6 3区 地球科学 Q1 MARINE & FRESHWATER BIOLOGY
N.M. Burns , D.M. Bailey , C.R. Hopkins
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引用次数: 0

Abstract

Marine seabed mapping is an important element in marine spatial and conservation planning. Recent large scale mapping programmes have greatly increased our knowledge of the seafloor, yet at finer resolutions, large gaps remain. Loch Eriboll, Scotland, is an area of conservation interest with a diverse marine environment supporting habitats and species of conservation importance. Here we test and present strategies for a predictive seabed substrata map for Loch Eriboll using drop down Stereo Baited Remote Underwater Video (SBRUV) imagery collected as part of systematic underwater survey of the Loch. A total of 216 SBRUV deployments were made across the study site in depths of 3 m–117 m, with six seabed classes identified using an adaptation of the EUNIS (European Nature Information System) hierarchical habitat classification scheme. Four statistical learning approaches were tested, we found, Generalised Additive Models (GAMs) provided the optimal balance between over- and underfitted predictions. We demonstrate the creation of a predictive substratum habitat map covering 63 km2 of seabed which predicts the probability of presence and relative proportion of substratum types. Our method enables naturally occurring edges between habitat patches to be described well, increasing the accuracy of mapping habitat boundaries when compared to categorical approaches. The predictions allow for both defined boundaries such as those between sand and rock and fuzzy boundaries seen among fine mixed sediments to exist in the same model structure. We demonstrate that SBRUV imagery can be used to generate cost effective, fine scale predictive substrata maps that can inform marine planning. The modelling procedure presented has the potential for a wide adoption by marine stakeholders and could be used to establish baselines for long term monitoring of benthic habitats and further research such as animal distribution and movement work which require detailed benthic maps.

预测性 GAM 海床地图可考虑明确和模糊的边界,以提高苏格兰海湖泊海景的准确性
海洋海底测绘是海洋空间和保护规划的重要内容。最近的大规模测绘计划大大增加了我们对海底的了解,但在更精细的分辨率方面,仍然存在很大差距。苏格兰的埃里波尔湖是一个具有保护意义的区域,其多样的海洋环境支持着具有重要保护意义的栖息地和物种。在此,我们利用在对埃里布尔湖进行系统水下勘测时收集的下拉立体诱饵遥控水下视频(SBRUV)图像,测试并提出了为埃里布尔湖绘制预测性海底底层地图的策略。我们在研究地点的 3 米-117 米水深范围内共进行了 216 次 SBRUV 部署,并采用 EUNIS(欧洲自然信息系统)分层生境分类方案确定了六个海底类别。我们对四种统计学习方法进行了测试,发现广义相加模型(GAMs)在预测过高和过低之间实现了最佳平衡。我们展示了覆盖 63 平方公里海底的预测性底层生境图,该图可预测底层类型的存在概率和相对比例。我们的方法能够很好地描述栖息地斑块之间自然形成的边缘,与分类方法相比,提高了绘制栖息地边界的准确性。预测结果允许在同一模型结构中同时存在明确的边界(如沙石之间的边界)和细小混合沉积物之间的模糊边界。我们证明了 SBRUV 图像可用于生成具有成本效益的精细比例预测底层地图,为海洋规划提供信息。所介绍的建模程序有可能被海洋利益相关者广泛采用,并可用于建立底栖生物栖息地长期监测基线和进一步研究,如动物分布和移动工作,这些工作都需要详细的底栖地图。
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来源期刊
CiteScore
5.60
自引率
7.10%
发文量
374
审稿时长
9 months
期刊介绍: Estuarine, Coastal and Shelf Science is an international multidisciplinary journal devoted to the analysis of saline water phenomena ranging from the outer edge of the continental shelf to the upper limits of the tidal zone. The journal provides a unique forum, unifying the multidisciplinary approaches to the study of the oceanography of estuaries, coastal zones, and continental shelf seas. It features original research papers, review papers and short communications treating such disciplines as zoology, botany, geology, sedimentology, physical oceanography.
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