the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Apr 2025
Ideas and perspectives: Using meta-omics to unravel biogeochemical changes from cell to planetary scales
Abstract. Increased human impacts on Earth systems are radically altering biogeochemical cycles. While long-term environmental observatories and Earth System Models (ESMs) provide valuable insights into the mechanisms of nutrient dynamics, their performance is limited at the fine spatial scales controlled by the functional diversity of plant and microbial communities. This gap in our understanding concerning the roles of microbial diversity and plant-microbial interactions in decomposition and nutrient dynamics extends across many global ecosystems. Recent advances in meta-omics technologies, including metagenomics, metatranscriptomics, metaproteomics, and metabolomics, offer a wide array of tools for assessing metabolic to genetic to evolutionary drivers of ecosystem functioning. Here, we explore the integration of meta-omics with traditional ecological approaches to examine responses to global environmental changes. We present case studies from diverse environments—soils, aquatic systems, clouds, and paleoarchives—demonstrating how meta-omics can unravel the roles of microbial diversity, metabolic pathways, and trait distributions critical to understanding greenhouse gas fluxes, nutrient cycling, and biogeochemistry. Although meta-omics is still beset with challenges including data heterogeneity arising from wide-ranging methods, omics-derived traits, kinetic parameters, and machine learning tools can be used to enhance ESM predictive capability. For example, emerging applications of meta-omics to ancient environmental DNA are extending our capacity to link historical patterns with future projections, offering a long-term perspective on ecosystem dynamics. This review highlights the potential of integrating omics with experimental manipulations alongside existing monitoring and modelling efforts to refine predictions of ecosystem responses to natural and anthropogenic-driven environmental changes. Because omics approaches cross a range of scientific domains, they could be used to foster collaboration and even integration within existing models, thus laying the foundation for informed conservation and ecosystem management strategies from local to global scales.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-1716', Anonymous Referee #1, 27 Sep 2025
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AC1: 'Reply on RC1', Elsa Abs, 16 Dec 2025
Reviewer 1 – Response
We thank the reviewer for their thoughtful and constructive comments, which help clarify the scope and strengthen the perspective.
Comment 1.
We agree that several biogeochemical variables (e.g. pH, organic matter content) can be directly measured and that sequencing does not replace such observations. In the revised manuscript, we will clarify that the added value of meta-omics is not to reconstruct measured state variables, but to parameterize, constrain, and project microbial processes and responses under changing environmental conditions (e.g. climate change, altered nutrient regimes). We will expand the discussion to explicitly contrast direct observation with metagenome-parameterized process representations, and provide concrete examples of how microbial functional potential and trait distributions can inform model behaviour beyond what static measurements allow.Comment 2.
We agree that the manuscript would benefit from a clearer overview of existing frameworks that already integrate microbial or enzyme information into biogeochemical modelling. In the revised version, we will add a concise summary of well-established approaches, including the MEND framework and GeoChip-based model calibration, and position our perspective relative to these efforts. This will help clarify how genome-to-ecosystem approaches complement and extend existing microbial–biogeochemical model linkages.Comment 3.
We appreciate the reviewer’s suggestions and agree that the section on biodiversity–ecosystem function linkages can be strengthened. In the revised manuscript, we will expand this discussion to explicitly describe emerging methodological approaches, including machine-learning-based mappings between microbial communities, genomic traits, and physicochemical variables, as well as trait-based and genome-informed inference of environmental preferences (e.g. pH). We will incorporate the suggested references and clarify how these approaches can be used to bridge microbial diversity, traits, and ecosystem-level functions.Citation: https://doi.org/10.5194/egusphere-2025-1716-AC1
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AC1: 'Reply on RC1', Elsa Abs, 16 Dec 2025
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RC2: 'Comment on egusphere-2025-1716', Wang Minxiao, 01 Dec 2025
The integration of genetic/omics data into Earth System Models (ESMs) is a promising direction to improve predictions of ecosystem evolution, particularly for element cycling and biodiversity–function relationships. This manuscript addresses a timely topic and provides a valuable forward-looking perspective. I recommend publication after minor-to-moderate revision, mainly to improve clarity and usefulness to readers.A key improvement would be to strengthen figure/box captions and add a few summary visuals/tables. Several sections contain important ideas (especially the model–omics linkage), but the presentation is sometimes too high-level for non-specialists to follow efficiently.
General / presentation
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Figures and boxes: Please provide more detailed captions for each figure/box so that they are informative as stand-alone items (what the panels show, what variables/axes represent, what the workflow inputs/outputs are, what assumptions/limitations apply).
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Add summary visuals: The manuscript proposes several frameworks/steps for future research; a small number of schematic summaries (one workflow diagram + one database table + one worked example box) would substantially improve readability.
Specific comments (by line / element)
L119: Typo/formatting: please correct 10 ^ 30 to 10^30 (or “10³⁰”). The sentence currently reads “est. 1030 cells on Earth”, which should be 10^30.
L128: Please mention the latest microbial database/resource published in Nature (2024) that is relevant to large-scale ocean microbiome catalogues. For example, the 2024 Nature study that constructed a unified global ocean microbiome genome catalogue (GOMC) from large-scale marine metagenomes would fit well here and is directly aligned with the manuscript’s message about global genomic catalogues.
L177: Please add quantitative detail (exact numbers/ranges) to support the statement about trait variation across taxonomic/biological levels and its consequences. For instance, in the Prochlorococcus example (ecotypes/strain clusters), it would help to report at least one numeric range (e.g., contribution estimate, temperature niche range, or projected shift magnitude) rather than only qualitative wording.
L215: I suggest revising the subsection title to “Example from an ocean system” and expanding the discussion with open-ocean examples. Many foundational concepts (e.g., mixotrophy, microbial loop) were developed in open-ocean systems and are now known to be important for carbon sequestration and broader element cycling. Adding 1–2 open-ocean examples would make this section more representative and historically accurate.
L220: Please explicitly mention the expansion and intensification of OMZs (oxygen minimum zones) under climate change (spatial expansion/shallower boundaries) and clarify how this affects redox-sensitive element cycling and microbial functional shifts.
Box 2: Omics studies also highlight the roles and risks of viruses in polar regions (e.g., viral impacts on microbial mortality, carbon shunting, lysogeny/reactivation risks in warming/ice melt contexts). Please add a short paragraph (and key citations) reflecting this.
~L255 (pathway annotation paragraph): This part is important for future research. I strongly recommend adding a small schematic chart/flow diagram summarizing the proposed strategy (marker genes → remote homology methods → phylogenetic validation → pathway completeness checks → operon/context evidence).
~L290: Please include a table listing recommended databases (genomes/MAG catalogues, functional annotation resources, metadata standards, etc.). Suggested columns: database name, data type (metagenome/MAG/metatranscriptome, viruses/euks), ecosystem coverage, key strengths/limitations, access/metadata standard, primary reference.
~L320 (core section): This is the heart of the review. I suggest adding a boxed worked example that walks the reader through one complete pipeline from omics to ESM parameters (e.g., genome → inferred traits → kinetic parameters → model output), including what the inputs are, what assumptions are made, and where uncertainty enters.
~L350: The text mentions the key complexity (typo in my notes: “kexy”) that may confuse many readers, including researchers outside the subfield. Please add a short, concrete summary of how complexities are handled/discriminated in practice, e.g., a bullet list of strategy + toolkits (QC, assembly/binning, dereplication, contamination checks, trait inference tools, uncertainty quantification, validation pathways). (If you discuss ancient DNA here, also include standard authenticity/discrimination criteria.)
Fig. 4: Consider revising/expanding the figure to connect more directly to ocean microbiome projects (e.g., Tara Oceans), and to show at least one example where omics data have been used (or could be used) to predict carbon dynamics (carbon export, remineralization, plankton network effects). The current text already frames Tara Oceans as the archetypal global survey and notes that Fig. 4 is an example of projecting end-century biodiversity anomalies; strengthening the omics→carbon-dynamics linkage would better match the manuscript’s thesis.
L449: I agree that metatranscriptomics can provide essential information on ecosystem responses to climate change. Please consider explicitly integrating metatranscriptome data into your proposed data-to-model framework (where it fits, what it resolves beyond metagenomes, and how it helps constrain “potential vs realized” function).
Citation: https://doi.org/10.5194/egusphere-2025-1716-RC2 -
AC2: 'Reply on RC2', Elsa Abs, 16 Dec 2025
Reviewer 2 – Response
We thank the reviewer for their careful reading of the manuscript and for the detailed and constructive suggestions. We are pleased that the reviewer finds the perspective timely and valuable. We agree that several sections would benefit from clearer presentation and additional schematic support, especially for readers outside the immediate omics–modelling community. We address the comments point by point below.
General / presentation
We agree that figures and boxes should be more informative as stand-alone elements. In the revised manuscript, we will expand all figure and box captions to clearly describe the panels, variables, workflow inputs and outputs, as well as key assumptions and limitations.
We also agree that additional summary visuals would substantially improve readability. As suggested, we will add (i) a schematic workflow diagram summarizing the proposed omics-to-model pipeline, (ii) a table compiling key databases and resources, and (iii) a boxed worked example illustrating one complete pathway from omics data to ESM-relevant parameters.
Specific comments
L119.
We will correct the formatting of the power notation to 1030.L128.
We will add a reference to the 2024 Nature global ocean microbiome genome catalogue and briefly describe its relevance for large-scale genomic resources aligned with Earth system modelling.L177.
We agree that quantitative context would strengthen this point. In the revised manuscript, we will add representative numerical ranges to illustrate trait variation across taxonomic and biological levels, including in the Prochlorococcus example.L215.
We will revise the subsection title to “Example from an ocean system” and expand the discussion to include open-ocean examples, acknowledging their historical and conceptual importance for microbial ecology and carbon cycling.L220.
We will explicitly discuss the expansion and shoaling of oxygen minimum zones under climate change and clarify the implications for redox-sensitive element cycling and microbial functional shifts. In Box 2, we will also add a short paragraph highlighting insights from omics studies on viral roles in polar regions, including impacts on microbial mortality and carbon processing, with appropriate citations.~L255 (pathway annotation paragraph).
We agree that a schematic summary would be helpful. In the revised version, we will add a concise flow diagram outlining the proposed pathway annotation strategy, from marker genes to pathway validation.~L290.
We will include a table summarizing recommended databases and resources, indicating data types, ecosystem coverage, strengths and limitations, metadata standards, and key references.~L320 (core section).
We agree that a worked example would help readers navigate the conceptual pipeline. We will add a boxed example walking through a representative case from omics data to inferred traits and model parameters, explicitly noting assumptions and sources of uncertainty.~L350.
We will add a short, concrete summary describing how methodological and conceptual complexity is handled in practice, including key quality-control steps, toolkits, and validation strategies. Where relevant, we will also clarify standard criteria used to distinguish authentic signals in ancient DNA studies.Fig. 4.
We agree that Fig. 4 could be more tightly linked to ocean microbiome initiatives and carbon-cycle applications. In the revised manuscript, we will revise the figure and/or its caption to more explicitly connect global microbiome surveys (e.g. Tara Oceans) with potential or demonstrated applications to carbon dynamics.L449.
We agree that metatranscriptomics deserves a clearer role in the proposed framework. In the revised version, we will explicitly integrate metatranscriptome data into the data-to-model workflow, clarifying how it complements metagenomes by constraining realized function and ecosystem responses.Citation: https://doi.org/10.5194/egusphere-2025-1716-AC2
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Major comments:
This perspective manuscript highlighted the microbial datasets based on meta-omics technologies, and explored the integration of such datasets into Earth System Models to examine the microbial responses to environmental changes. Some key points and future efforts are discussed in the current version, and this would raise some concerns of ecologists to considering the important effects of microbiomes. The manuscript was written well to enjoy the reading, I have only some open concerns for specific issues.