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Finding the hot-spots within a biodiversity hotspot: fine-scale biological predictions within a submarine canyon using high-resolution acoustic mapping techniques

机译:寻找生物多样性热点内的热点:使用高分辨率声学绘图技术在海底峡谷内进行精细尺度的生物预测

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摘要

Submarine canyons are complex geomorphological features that have been suggested as potential hotspots for biodiversity. However, few canyons have been mapped and studied at high resolution (tens of m). In this study, the four main branches of Whittard Canyon, Northeast Atlantic, were mapped using multibeam and sidescan sonars to examine which environmental variables were most useful in predicting regions of higher biodiversity. The acoustic maps obtained were ground truthed by 13 remotely operated vehicle (ROV) video transects at depths ranging from 650 to 4000 m. Over 100 h of video were collected, and used to identify and georeference megabenthic invertebrate species present within specific areas of the canyon. Both general additive models (GAMs) and random forest (RF) were used to build predictive maps for megafaunal abundance, species richness and biodiversity. Vertical walls had the highest diversity of organisms, particularly when colonized by cold-water corals such as Lophelia pertusa and Solenosmilia variabilis. GAMs and RF gave different predictive maps and external assessment of predictions indicated that the most adequate technique varied based on the response variable considered. By using ensemble mapping approaches, results from more than one model were combined to identify vertical walls most likely to harbour a high biodiversity of organisms or cold-water corals. Such vertical structures were estimated to represent less than 0.1% of the canyon's surface. The approach developed provides a cost-effective strategy to facilitate the location of rare biological communities of conservation importance and guide further sampling efforts to help ensure that appropriate monitoring can be implemented.
机译:海底峡谷是复杂的地貌特征,被认为是生物多样性的潜在热点。但是,很少有峡谷以高分辨率(数十米)进行绘制和研究。在这项研究中,使用多波束和侧扫声纳绘制了东北大西洋惠塔德峡谷的四个主要分支,以检查哪些环境变量最有助于预测生物多样性较高的地区。通过13个遥控车辆(ROV)视频样带在650至4000 m的深度对地面获得的声图进行了地面实测。收集了超过100小时的视频,并将其用于识别和地理参考峡谷特定区域内存在的大型底栖无脊椎动物。通用加性模型(GAM)和随机森林(RF)均用于建立大型动物丰度,物种丰富度和生物多样性的预测图。垂直的壁具有最高的生物多样性,特别是在被Lophelia pertusa和Solenosmilia variabilis等冷水珊瑚定居时。 GAM和RF提供了不同的预测图,对预测的外部评估表明,最适当的技术根据所考虑的响应变量而有所不同。通过使用集成映射方法,可以将来自多个模型的结果组合起来,以识别最有可能掩盖生物或冷水珊瑚高度生物多样性的垂直墙。据估计,此类垂直结构占峡谷表面的比例不到0.1%。所开发的方法提供了一种具有成本效益的策略,以促进对具有保护意义的稀有生物群落的定位,并指导进一步的采样工作,以帮助确保可以实施适当的监测。

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