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Autonomous Ecological Surveying of the Abyss (AESA): Understanding Mesoscale Spatial Heterogeneity in the Deep Sea

深渊自主生态测量（AESA）：了解深海中尺度空间异质性

基本信息

批准号：
NE/H021787/1
负责人：
Henry Ruhl
金额：
$ 49.56万
依托单位：
NATIONAL OCEANOGRAPHY CENTRE
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FH021787%2F1
关键词：
Autonomous Ecological Surveying Abyss AESA

项目摘要

Determining the distribution and abundance of life is challenging, especially in the deep sea where high pressure and other logistical challenges limit data availability to a tiny fraction of what is available for other systems. Most of Earth's surface is nonetheless covered by water > 2000 m deep. Life in these abyssal regions directly influences the burial of carbon and nutrient cycling. Long-term research has now shown that even larger animals in the deep sea can vary in density by orders of magnitude, with concurrent changes in average body size, over periods as short as months. These variations are widely believed to be linked to climate-driven variation in the food supply to the deep sea. Similarly, biogeography studies have found that over distances approaching 100 km or more, the abundance of deep-sea life is related to surface productivity in the waters above. Thus the deep sea could be readily impacted by processes that alter surface ocean conditions like climate change, fishery activity, or ocean iron fertilisation. While there has been an increase in the understanding of how climate and surface processes affect deep-sea communities, the ability to understand these links further is thought to be limited by sampling error from undetected habitat heterogeneity (i.e. irregular or uneven habitat distributions). Features like hills, valleys, depressions, small rock outcrops, and biogenic mounds add to habitat complexity, but links between such features and the animals that live among them are very poorly resolved in abyssal plain habitats using current methods. We propose a new approach using the autonomous underwater vehicle (AUV) Autosub6000 to survey ecologically the Porcupine Abyssal Plain (PAP) Sustained Observatory to address a key question: Are spatial patterns in abyssal habitat features (like bathymetry, seafloor cover of phytodetrius [i.e. food availability], suspended solid concentration) related to spatial patterns in photographed life (density, dispersion, or biodiversity) at spatial scales from <1 m^2 to about 100 km^2? The effort is timely because we plan to supplement an existing Oceans2025 cruise to the PAP in 2011. We will use Autosub6000 to create a detailed bathymetric map of the study area. We will then use a camera system integrated with Autosub6000 to conduct photographic surveys over a 1 km^2 and 100 km^2 area, each with synchronous collection of oceanographic and environmental data. A series of sediment samples will also be collected to examine differences in sediment quality between higher and lower lying areas. A landscape (seascape) ecology database will then be assembled for hypothesis testing. We expect that seafloor features like deep-sea mounds, hills, and depressions will relate to non-random distributions of food availability and the photographed life. We expect that as the scale of features such as hills vary, so will the scale of patterns of some animals including fish. We expect that the results will help explain previous sampling error and allow for an order of magnitude improvement in the accuracy of abundance and distribution estimates, as well as the accuracy of ecosystem models that are based on those data. We will use respiration rates (i.e. food demand and carbon dioxide release) and sediment mixing indicators measured in Oceans2025 and other NOCS projects, and the abundance and size measures collected here to create maps of ecological function. This will show how factors such as hills, food supply, or community composition relate spatially to respiration and sediment mixing. That knowledge will provide important insight into how spatially pervasive temporal climate change impacts might be, a significant input for ecosystem and carbon budget modelling. Our effort will also have impacts on future national survey capability and the ability of researchers to convey information about deep-sea habitats to government, industry, students, and the public.

确定生命的分布和丰度具有挑战性，特别是在深海，高压和其他后勤挑战将数据可用性限制在其他系统可用数据的一小部分。然而，地球表面的大部分被超过2000米深的水覆盖。这些深海区域的生命直接影响碳的埋藏和养分循环。长期研究现已表明，即使是深海中较大的动物，其密度也会有几个数量级的变化，同时平均体型也会在短短几个月的时间内发生变化。人们普遍认为，这些变化与气候驱动的深海食物供应变化有关。同样，海洋地理学研究发现，在接近100公里或更远的距离上，深海生物的丰度与上述沃茨的表面生产力有关。因此，深海很容易受到气候变化、渔业活动或海洋铁肥化等改变海洋表面条件的过程的影响。虽然对气候和海面过程如何影响深海群落的了解有所增加，但进一步了解这些联系的能力被认为受到未发现的生境异质性（即生境分布不规则或不均匀）造成的抽样误差的限制。丘陵、山谷、洼地、小岩石露头和生物土丘等特征增加了栖息地的复杂性，但使用目前的方法，在深海平原栖息地中，这些特征与生活在其中的动物之间的联系很难解决。我们提出了一种新的方法，使用自主水下航行器（AUV）Autosub 6000对豪猪深海平原（PAP）持续观测站进行生态调查，以解决一个关键问题：是深海生境特征的空间模式（如水深测量、海底植被覆盖[即食物供应]、悬浮固体浓度）（密度，分散度或生物多样性）在空间尺度从<1平方米到约100平方公里？这一努力是及时的，因为我们计划在2011年将现有的海洋2025巡航补充到PAP。我们将使用Autosub6000创建研究区域的详细测深图。然后，我们将使用与Autosub6000集成的相机系统在1平方公里和100平方公里的区域内进行摄影调查，每次都同步收集海洋和环境数据。此外，亦会收集一系列沉积物样本，以研究地势较高和较低地区沉积物质素的差异。一个景观（海景）生态数据库，然后将组装的假设检验。我们预计，深海土丘、丘陵和洼地等海底特征将与食物供应和拍摄到的生命的非随机分布有关。我们预计，随着丘陵等特征的规模变化，包括鱼类在内的一些动物的模式规模也会变化。我们预计，这些结果将有助于解释以前的抽样误差，并使丰度和分布估计的准确性以及基于这些数据的生态系统模型的准确性有一个数量级的提高。我们将使用呼吸率（即食物需求和二氧化碳释放）和沉积物混合指标在海洋2025和其他NOCS项目中测量，以及这里收集的丰度和大小测量来创建生态功能地图。这将显示如何因素，如丘陵，食物供应，或社区组成空间呼吸和沉积物混合。这一知识将为了解气候变化的时空影响可能具有多大的空间普遍性提供重要的见解，这是对生态系统和碳预算建模的重要投入。我们的努力也将对未来的国家调查能力和研究人员向政府、工业、学生和公众传达深海栖息地信息的能力产生影响。