More than co-occurrence: what amplicon time series data can tell us

IF 3.3 3区 地球科学 Q2 OCEANOGRAPHY
Ellen Oldenburg , Nima P. Saadat , Sofie Thielen , Ovidiu Popa
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引用次数: 0

Abstract

The Arctic Ocean is undergoing unprecedented transformations due to climate change, with rising temperatures, shrinking sea ice, and shifting oceanographic conditions reshaping its ecosystem. At the center of these changes lie the Arctic's microbial communities that drive biogeochemical cycles, sustain primary production, and maintain ecosystem stability. Long-term ecological research is essential for understanding microbial community dynamics and their role in biogeochemical cycles, particularly in polar ecosystems. The HAUSGARTEN observatory, established in 1999, has provided unparalleled insights into Arctic marine microbial ecology. This review synthesizes 25 years of microbial re-search at HAUSGARTEN, revealing how advanced methodologies—such as amplicon sequencing, which has revolutionized microbial ecology by enabling the taxonomic characterization of complex communities through targeted marker genes, Fourier decomposition, Convergent Cross Mapping (CCM), and Energy Landscape Analysis (ELA)—have revolutionized our understanding of Arctic microbial ecology. By integrating time-series data with network-based approaches, we move beyond static snapshots to uncover the hidden rhythms of microbial life, from seasonal successions to long-term trends. We explore the interplay between environmental drivers and microbial community structure, emphasizing seasonal succession, functional adaptations, and the impact of Atlantification. Environmental conditions are constantly changing; therefore, there is a need for predictive models. By combining machine learning, deterministic modeling, and ecological theory, we are now poised to forecast how microbial communities will respond to future climate scenarios. From Graph Neural Networks (GNNs) to ARIMA forecasting, this review showcases the power of amplicon data in a time series frame work together with interdisciplinary approaches to tackle one of the most pressing challenges of our time. Arctic microbial communities are the key to understanding and mitigating the impacts of climate change, and this review is a guide to unlocking their secrets.
不仅仅是共现:放大时间序列数据能告诉我们什么
由于气候变化,北冰洋正在经历前所未有的变化,气温上升、海冰萎缩、海洋条件变化重塑了其生态系统。这些变化的核心是北极的微生物群落,它们推动生物地球化学循环,维持初级生产,维持生态系统稳定。长期生态学研究对于理解微生物群落动态及其在生物地球化学循环中的作用至关重要,特别是在极地生态系统中。HAUSGARTEN观测站成立于1999年,提供了对北极海洋微生物生态的无与伦比的见解。这篇综述综合了HAUSGARTEN 25年的微生物研究,揭示了先进的方法,如扩增子测序,它通过目标标记基因、傅立叶分解、收敛交叉映射(CCM)和能量景观分析(ELA)来实现复杂群落的分类特征,从而彻底改变了微生物生态学,彻底改变了我们对北极微生物生态学的理解。通过将时间序列数据与基于网络的方法相结合,我们超越了静态快照,揭示了微生物生命的隐藏节奏,从季节性演替到长期趋势。我们探讨了环境驱动因素与微生物群落结构之间的相互作用,强调了季节演替、功能适应和大西洋化的影响。环境条件不断变化;因此,有必要建立预测模型。通过结合机器学习、确定性建模和生态学理论,我们现在准备预测微生物群落将如何应对未来的气候情景。从图神经网络(gnn)到ARIMA预测,本综述展示了时间序列框架中放大数据的力量,以及跨学科方法,以解决我们这个时代最紧迫的挑战之一。北极微生物群落是理解和减轻气候变化影响的关键,这篇综述是解开它们秘密的指南。
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来源期刊
CiteScore
6.40
自引率
16.70%
发文量
115
审稿时长
3 months
期刊介绍: Deep-Sea Research Part II: Topical Studies in Oceanography publishes topical issues from the many international and interdisciplinary projects which are undertaken in oceanography. Besides these special issues from projects, the journal publishes collections of papers presented at conferences. The special issues regularly have electronic annexes of non-text material (numerical data, images, images, video, etc.) which are published with the special issues in ScienceDirect. Deep-Sea Research Part II was split off as a separate journal devoted to topical issues in 1993. Its companion journal Deep-Sea Research Part I: Oceanographic Research Papers, publishes the regular research papers in this area.
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