Assessing species distributions and morphometrics in Southern Ocean diatoms using high throughput imaging and semi-automated image analysis

硅藻是南大洋（SO）的主要初级生产者，强烈影响着这一广泛的远洋栖息地的生态和地球化学过程。无论是个别物种的出现，其中一些形态特征，都与水团的性质，并通过后者，气候变化。因此，组合组成和物种特异性形态测量学被大量用作过去气候变化的指标。另一方面，持续的气候变化影响SO海洋学，并通过它，也硅藻分布，这反过来又影响食物网结构和生物地球化学循环。光学显微镜（LM）是研究生物与环境关系的主要工具，无论是用于代理的开发/应用，还是用于了解当前和未来的分布范围。以传统方式实践的LM具有许多问题，包括低通量、对高素质分类专家的要求以及与数据存档/质量和再现性相关的问题。为了解决这些问题，该项目的申请人最近开始了一项合作，将高通量光学显微镜和自动图像分析方法结合起来，用于硅藻的形态表征和组合成分评估。在此次合作的前9个月内，我们成功建立了在永久载玻片上对硅藻标本进行高通量半自动显微成像的工作流程;开发了软件（出版中）用于硅藻瓣的自动检测和轮廓表征，为手动交互和质量控制提供了可能性;测试了从硅藻图像中提取纹理特征;并基于通过上述方法提取的形态测量特征，使用机器学习方法对硅藻的分类学分类进行第一次测试。这些方法使我们能够拍摄和分析大量的硅藻载玻片，结合自动化方法的通量和手动质量控制的严格性。在这里，我们建议将这些方法应用到广泛收集的SO样品的胡斯特硅藻研究中心的地理分布和形态特征丰富的浮游SO硅藻物种，并将它们与环境参数使用统计技术。这项工作将使我们能够首次使用硅藻物种的存在-不存在数据来验证分布模型，并探索与多个物种的环境参数相关的形态学趋势，这可能为开发强大的古海洋学代理提供新的可能性。

Diatoms are the dominant primary producers of the Southern Ocean (SO), strongly influencing ecological and biogeochemical processes in this extensive pelagic habitat. Both occurrence of individual species, and morphometric features of some of them, are related to water mass properties, and, through the latter, to climatic changes. Accordingly, both assemblage composition and species-specific morphometrics are heavily used as indicators of past climatic changes. On the other hand, ongoing climate change affects SO oceanography, and through that, also diatom distributions, which in turn affect food web structure and biogeochemical cycles. The central tool in studies of diatom-environment relationships has been light microscopy (LM), whether for development / application of proxies or for learning about current and future distribution ranges. LM, practiced in the traditional way, has numerous issues, including low throughput, requirement of highly qualified taxonomic experts, and problems related data archival / quality and reproducibility. To address these issues, the applicants of this project have recently started a collaboration bringing together high throughput light microscopy and automated image analysis methods for morphometric characterization and assemblage composition assessment of diatoms. Within the first 9 months of this collaboration, we successfully established workflows for high throughput, semi-automated microscopic imaging of diatom specimens on permanent slides; developed software (under publication) for automated detection and outline characterization of diatom valves providing the possibility for manual interaction and quality control; tested the extraction of texture features from diatom images; and performed first tests using machine learning methods for the taxonomic classification of diatoms based on the morphometric features extracted by the above methods. These methods allow us to photograph and analyze large numbers of diatom slides combining the throughput of automated methods with the stringency of manual quality control. Here we propose to apply these methods to the extensive collection of SO samples of the Hustedt Diatom Study Centre to characterize the geographic distribution and morphometrics of abundant planktonic SO diatom species, and to relate them to environmental parameters using statistical techniques. The work proposed will allow us to validate distribution models using presence-absence data for the first time for diatoms species, and to explore morphometric trends related to environmental parameters across multiple species, which might open novel possibilities for the development of robust paleo-oceanographic proxies.