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

确定生命的分布和丰富程度是具有挑战性的，特别是在深海，那里的高压和其他后勤挑战限制了数据的可用性，只有其他系统可用数据的一小部分。尽管如此，地球的大部分表面都被大约2000米深的水覆盖着。这些深海地区的生命直接影响着碳的埋藏和营养循环。长期研究表明，即使是深海中的大型动物，在短至几个月的时间内，密度也会发生数量级的变化，同时平均体型也会发生变化。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，生物地理学研究发现，在接近100公里或更远的距离上，深海生物的丰富程度与上面水域的表面生产力有关。因此，深海很容易受到改变海洋表层条件的过程的影响，比如气候变化、渔业活动或海洋铁肥。虽然对气候和表面过程如何影响深海群落的理解有所增加，但进一步理解这些联系的能力被认为受到未检测到的栖息地异质性（即不规则或不均匀的栖息地分布）的采样误差的限制。丘陵、山谷、洼地、小岩石露头和生物丘等特征增加了栖息地的复杂性，但这些特征与生活在其中的动物之间的联系，用现有的方法在深海平原栖息地中很难解决。我们提出了一种使用自主水下航行器（AUV） Autosub6000对Porcupine Abyssal Plain （PAP）持续观测站进行生态调查的新方法，以解决一个关键问题：深海栖息地特征的空间模式（如测深，植物碎屑的海底覆盖[即食物可用性]，悬浮固体浓度）是否与从<1 m^2到约100 km^2的空间尺度上拍摄的生命的空间模式（密度，分散或生物多样性）相关？这项努力是及时的，因为我们计划在2011年补充现有的“海洋2025”巡航。我们将使用Autosub6000来创建研究区域的详细水深图。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，每个区域同步收集海洋和环境数据。还将收集一系列沉积物样本，以检查地势较高和较低地区之间沉积物质量的差异。然后将构建景观（海景）生态学数据库进行假设检验。我们期望像深海土丘、山丘和洼地这样的海底特征将与食物供应和被拍摄的生命的非随机分布有关。我们预计，随着山体等特征的尺度变化，包括鱼类在内的一些动物图案的尺度也会变化。我们期望这些结果将有助于解释以前的抽样误差，并允许在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方面有一个数量级的提高。我们将使用“海洋2025”和其他国家石油公司项目中测量的呼吸速率（即食物需求和二氧化碳释放）和沉积物混合指标，以及这里收集的丰度和大小测量来创建生态功能地图。这将显示诸如山丘、食物供应或群落组成等因素如何在空间上与呼吸和沉积物混合有关。这些知识将提供重要的见解，了解气候变化的时空影响可能是如何普遍存在的，这是生态系统和碳预算建模的重要投入。我们的努力还将对未来的国家调查能力和研究人员向政府、工业、学生和公众传达有关深海栖息地信息的能力产生影响。

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.