Dokument: More than co-occurrence: what amplicon time series data can tell us
| Titel: | More than co-occurrence: what amplicon time series data can tell us | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=72355 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20260219-130645-1 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Oldenburg, Ellen [Autor] Saadat, Nima P. [Autor] Thielen, Sofie [Autor] Popa, Ovidiu [Autor] | |||||||
| Dateien: |
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| Beschreibung: | 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. | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Oldenburg, E., Saadat, N., Thielen, S., & Popa, O. (2026). More than co-occurrence: what amplicon time series data can tell us. Deep-Sea Research 2, 226, Article 105599. https://doi.org/10.1016/j.dsr2.2026.105599 | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät | |||||||
| Dokument erstellt am: | 19.02.2026 | |||||||
| Dateien geändert am: | 19.02.2026 |
