Hochfeld and colleagues promote the utilization of sediment archives to enhance modeling efforts. Specifically, this thesis supports the integration of evolutionary processes, which require consideration of extended temporal scales that are typically unattainable through conventional longitudinal datasets, such as mesocosms. By incorporating sediment archives, researchers can gain valuable insights into historical ecological dynamics and better understand the long-term impacts of environmental changes on species evolution and community structure.

The manuscript is informative and emphasizes their hypothesis in the context of phytoplankton, evaluates the prevailing literature, and elucidates the potential insights that may be derived from sediment archives. It provides an exposition of various datasets and delineates the methodologies through which diverse knowledge can be obtained. Furthermore, it articulates a modeling pipeline designed to enhance the synthesis of sediment archive information, thereby facilitating improved predictive outcomes. This comprehensive approach highlights the significance of sediment archives in ecological research and paves the way for future studies to integrate these valuable datasets into broader environmental assessments.

Specific comments:

• It is frequently noted that although the primary emphasis is on phytoplankton, the methodological framework could be applied to other biological entities. Incorporating the constraints associated with each dataset concerning alternative organisms may be beneficial. For example, the implications of microfossils may not be extendable to heterotrophic bacterial populations. Incorporating these constraints will provide a more nuanced understanding of the ecological dynamics at play, ensuring that researchers can accurately interpret the data and its relevance to different biological communities. Similarly, I recommend enhancing the limitations for (heterotrophic) prokaryotic systems, as following the Falkowski paradigm (i.e., microbial engines), these organisms are central for climate mitigations. Expanding the scope to include limitations will enhance the comprehensiveness of the assessments, allowing for a more holistic view of microbial interactions and their implications in various ecosystems.
• On another point, I would recommend adding a more extensive discussion about model plasticity. Plasticity could take the form of community structure modification, better data fitting, and new modeling techniques that could better embed more diverse data (?) with the potential risk of overfitting.
•  The manuscript would gain considerable value from including a more formalized definition of adaptation, particularly concerning contemporary modeling techniques. Specifically, it would be beneficial to incorporate an additional figure elucidating the rationale underlying this concept, which is frequently overlooked. Furthermore, section 3 would significantly enhance its clarity by providing a more adequate representation of the rationale behind models of evolutionary ecosystems. This could be achieved by including a box or a figure panel illustrating where (e.g., equations or parameter values) the model stands to gain from integrating both new and historical data. Moreover, it is essential to articulate how changes in ecosystem structure can be formalized. This would facilitate a deeper understanding of the model's dynamics and highlight the importance of adaptive management strategies in response to environmental shifts.
•  Section 2.3.3, entitled “Resurrection Experiments,” presents an intriguing perspective; however, it does not seamlessly integrate with the overall narrative of the manuscript. To mitigate this disruption in the continuity of the storyline, would it be possible to elaborate on the methodology for integrating this supplementary knowledge into the modeling discussions? In particular, could lines 206-210 further expand the content? Additionally, what approaches could be employed to establish a connection between GWAS analysis and the identified characteristics (i.e., growth traits)?
• In Figure 2, I would add a color code to the data that matches the one in Figure 1. This is a cosmetic proposition, but it would make the figures more connected, cohesive, and visually appealing.
• DNA sediment data, like DNA itself, is compositional in nature. Mixing such data with quantitative measurements for parameter assessments introduces limitations, which the authors should explicitly acknowledge.
• The distinction between model structure and parameter values could be elaborated further for clarity. A more detailed explanation would help readers understand the conceptual differences, as the proposed modeling pipeline figure covers mainly parameterization issues.