The large-scale oceanic distribution of trace elements: disentangling preformed contributions, regenerative processes, subsurface sources and sinks

存在于海水中的微量元素不影响盐度，被称为“微量元素”。微量金属虽然丰度很低，但在控制海洋生态系统动态方面却能发挥不成比例的巨大作用。这是因为有些是重要的酶和蛋白质正常运作所必需的，因此必须提供足够的数量来维持浮游植物的数量。海洋中有一些地区的关键金属，特别是铁（Fe），与其他营养物质相比缺乏，这限制了生物生产力。为什么一些地区缺乏某些微量营养素而另一些地区没有，目前还不完全清楚；支配这些金属自然循环的过程尚不为人所知。这种理解上的差距部分是由于测量海洋中痕量金属的困难：痕量金属的数量非常少，而水样是从金属研究船上采集的，这使得测量容易受到污染。常规测量海水中痕量金属的可靠技术直到最近才发展起来。多亏了他们，在过去几年里，海洋数据的准确性和数据覆盖范围得到了极大的提高。在这个项目中，我的目标是通过分析最新和最完整的痕量金属数据库，提高对痕量金属循环，特别是微量营养素的普遍理解。当试图解释深海中溶解的微量金属或其他营养物质浓度的测量结果时，困难在于人们不能轻易区分存在的数量，因为它已经从其他地方运送到采样点，以及由于当地内部过程而增加或减少的数量。然而，人们必须能够分离出后一种成分，以量化和解释地下生物地球化学过程对痕量金属循环的影响。如果没有这种能力，人们对所测浓度场的解释可能是错误的，将输运现象误认为是内部循环机制。这项工作将通过应用统计反褶积技术来明确量化运输的数量，直接解决这个问题。通过测量浓度和计算输运组分之间的差值，可以量化由于生物地球化学过程引起的部分，并绘制这些残留量。影响海洋中微量金属分布的最重要的过程之一是“清除”；这是溶解的金属附着在粒子上并随之下沉的倾向。清除对金属的影响大于其他营养物质。这是一个重要的过程，因为它无处不在（粒子无处不在），可以重新分配海洋内部的金属。据推测，如果清除能力强，或长时间运作，清除过程可以相对于其他营养物质分离金属。当受到这一过程强烈影响的土层被运回地表时，它们会带来相对于其他营养物质而言金属含量被耗尽的水。如果金属丰度太低，这将限制表面生产力。初步的模拟实验支持这样一种观点，即清除作用对某些金属（如钍、铍、稀土元素和铝）的分布具有一级控制作用。然而，目前尚不清楚微量营养素金属清除多少，以及这种效应是否能够解释微量营养素限制区域的分布和特征。这个项目将检验这个假设。首先，数据的统计反褶积结果将告知清除对每种金属施加的分馏程度。其次，利用模型模拟清除和分馏过程，量化其对地表生态系统的影响。

Elements present in seawater in quantities so small that they do not affect salinity are called "trace elements". In spite of their low abundances, trace metals can play disproportionally large roles in controlling the dynamics of marine ecosystems. This is because some are necessary for the proper functioning of important enzymes and proteins and must thus be supplied in sufficient quantities to maintain phytoplankton populations. There are regions in the ocean where key metals, particularly iron (Fe), are lacking in proportion to the other nutrients, what limits biological productivity. Why some micronutrients are lacking in some region and not others is not fully understood; the processes that govern the natural cycles of these metals are not well known. This gap in understanding is partly due to the difficulty of measuring trace metals in the ocean: trace metals are present in very small quantities and water samples are taken from metallic research ships, making measurements prone to contamination. Reliable techniques to routinely measure trace metals in seawater have only been recently developed. Thanks to them, the accuracy of the data and data coverage over the oceans have improved dramatically over the last few years. With this project, I aim to improve the general understanding of the cycling of trace metals, particularly the micronutrients, by analyzing the newest and most complete trace metal databases available.The difficulty when trying to interpret measurements of dissolved trace metal concentrations, or other nutrients, in the deep sea is that one cannot easily distinguish between the amount that is present because it has been transported to the point of sampling from somewhere else and the amount that has been added or removed due to local, internal processes. Yet, one must be able to isolate that later component to quantify and interpret the influence of subsurface biogeochemical processes on trace metal cycles. Without this ability, one's interpretation of the measured concentration field could be wrong, mistaking transport phenomena for internal cycling mechanisms. This work will directly address this issue by applying statistical deconvolution techniques to explicitly quantify the amount that is transported. By taking the difference between the measured concentrations and the calculated transported component, it is possible to quantify the fraction that is due to biogeochemical processes and map these residual quantities.One of the most important processes influencing the distribution of trace metals in the sea is "scavenging"; that is the propensity for dissolved metals to stick to particles and sink along with them. Scavenging affects metals more than other nutrients. It is an important process because it is omnipresent (particles are everywhere) and can redistribute the metals within the ocean interior. It is hypothesized that if scavenging is strong, or operating for a long time, the scavenging process can fractionate metals relative to the other nutrients. When layers that are affected strongly by this process are transported back to the surface, they will bring with them waters that are depleted in the metal relative to the other nutrients. If the metal abundance is too low, this will limit surface productivity. Preliminary modeling experiments support the view that scavenging exerts a first order control on the distribution of some metals, such as thorium, beryllium, the rare earth elements and aluminium. It is, however, not clear how much micronutrient metals scavenge and if this effect is able to explain the distribution and characteristics of micronutrient-limited regions. This project will test this hypothesis. First, the statistical deconvolution results from the data will inform on the degree of fractionation imposed by scavenging on each metal. Secondly, models will be used to simulate scavenging and the fractionation process and quantify the influence on surface ecosystems.