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Dynamic Nitrogen Resorption Improves Predictions of Nitrogen Cycling Responses to Global Change in a Next Generation Ecosystem Model.

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Title: Dynamic Nitrogen Resorption Improves Predictions of Nitrogen Cycling Responses to Global Change in a Next Generation Ecosystem Model.
Authors: Sophia, Gabriela1,2,3 (AUTHOR) gsophia@bgc-jena.mpg.de, Caldararu, Silvia4 (AUTHOR), Stocker, Benjamin D.3,5 (AUTHOR), Zaehle, Sönke1,6 (AUTHOR)
Source: Journal of Advances in Modeling Earth Systems. Jan2026, Vol. 18 Issue 1, p1-20. 20p.
Subject Terms: *Nutrient cycles, *Climate change, Resorption (Physiology), Atmospheric models, Ecosystem dynamics, Environmental history, Plant productivity, Deciduous plants
Abstract: Nutrient resorption from senescing leaves can significantly affect plant nutrient status and growth, making it an important process for carbon‐cycle predictions for land surface models (LSMs). Based on a recent analysis of global nutrient resorption patterns from trait data, we develop a dynamic scheme of nitrogen (N) resorption driven by leaf structural and environmental factors, and test its effect on present‐day global simulations for woody plant functional types (PFTs) using the QUINCY biosphere model. Consistent with observations, we predict higher N resorption for the deciduous PFT compared to the evergreen PFTs, while at the same time reproducing the global gradient of decrease in resorption with key environmental drivers such as air temperature within each PFT. As a result, the novel scheme increases N resorption in N‐limited plants, enhancing stored N for the subsequent year and reducing internal N limitation. This has cascading implications for ecosystem nutrient pools, plant productivity and, to a limited extent, the response of carbon and N cycling to elevated CO2. The new scheme contributes to the development of an ecologically realistic representation of nutrient resorption in an LSM, with implications for both present day and future N limitation of the terrestrial biosphere. Plain Language Summary: Nitrogen (N) is an essential nutrient for plant growth. To conserve it, plants have developed a strategy to recycle N from older leaves before they fall—a process known as nutrient resorption. This internal recycling can help them stay productive, especially under N‐limited conditions. In our study, we developed a novel realistic model that lets plants adjust how much N they reabsorb, based on their leaf construction costs and how much N is available in their environment. We used a numerical model that simulates ecosystem functioning to test how this flexible strategy affects plant growth and nutrient use across different plant types and N limitation status. Our results showed that the model reflects the real‐world patterns more accurately—for example, deciduous trees recycle more N than evergreens, and resorption levels shift with climate conditions. The model also shows that plants in N‐limited environments benefit from storing more recycled N, which supports growth in the following year. While the impact on growth under elevated CO2 is modest, the improved N resorption still helps alleviate internal limitations. This enhances how we represent plant nutrition in land surface models and supports better predictions of how ecosystems may respond to future climate change. Key Points: The novel scheme captures the overall key features of the global resorption gradient across biomes and between leaf habitsCompared to the fixed model, the dynamic scheme enhances N resorption and mitigates N limitation, increasing biomass in N‐limited plantsUnder elevated CO2, enhanced N resorption alleviates internal N limitation in non‐N‐limited systems, with limited biomass gains [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Dynamic Nitrogen Resorption Improves Predictions of Nitrogen Cycling Responses to Global Change in a Next Generation Ecosystem Model.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advances+in+Modeling+Earth+Systems%22">Journal of Advances in Modeling Earth Systems</searchLink>. Jan2026, Vol. 18 Issue 1, p1-20. 20p.
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  Data: *<searchLink fieldCode="DE" term="%22Nutrient+cycles%22">Nutrient cycles</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Resorption+%28Physiology%29%22">Resorption (Physiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystem+dynamics%22">Ecosystem dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+history%22">Environmental history</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+productivity%22">Plant productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Deciduous+plants%22">Deciduous plants</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Nutrient resorption from senescing leaves can significantly affect plant nutrient status and growth, making it an important process for carbon‐cycle predictions for land surface models (LSMs). Based on a recent analysis of global nutrient resorption patterns from trait data, we develop a dynamic scheme of nitrogen (N) resorption driven by leaf structural and environmental factors, and test its effect on present‐day global simulations for woody plant functional types (PFTs) using the QUINCY biosphere model. Consistent with observations, we predict higher N resorption for the deciduous PFT compared to the evergreen PFTs, while at the same time reproducing the global gradient of decrease in resorption with key environmental drivers such as air temperature within each PFT. As a result, the novel scheme increases N resorption in N‐limited plants, enhancing stored N for the subsequent year and reducing internal N limitation. This has cascading implications for ecosystem nutrient pools, plant productivity and, to a limited extent, the response of carbon and N cycling to elevated CO2. The new scheme contributes to the development of an ecologically realistic representation of nutrient resorption in an LSM, with implications for both present day and future N limitation of the terrestrial biosphere. Plain Language Summary: Nitrogen (N) is an essential nutrient for plant growth. To conserve it, plants have developed a strategy to recycle N from older leaves before they fall—a process known as nutrient resorption. This internal recycling can help them stay productive, especially under N‐limited conditions. In our study, we developed a novel realistic model that lets plants adjust how much N they reabsorb, based on their leaf construction costs and how much N is available in their environment. We used a numerical model that simulates ecosystem functioning to test how this flexible strategy affects plant growth and nutrient use across different plant types and N limitation status. Our results showed that the model reflects the real‐world patterns more accurately—for example, deciduous trees recycle more N than evergreens, and resorption levels shift with climate conditions. The model also shows that plants in N‐limited environments benefit from storing more recycled N, which supports growth in the following year. While the impact on growth under elevated CO2 is modest, the improved N resorption still helps alleviate internal limitations. This enhances how we represent plant nutrition in land surface models and supports better predictions of how ecosystems may respond to future climate change. Key Points: The novel scheme captures the overall key features of the global resorption gradient across biomes and between leaf habitsCompared to the fixed model, the dynamic scheme enhances N resorption and mitigates N limitation, increasing biomass in N‐limited plantsUnder elevated CO2, enhanced N resorption alleviates internal N limitation in non‐N‐limited systems, with limited biomass gains [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Advances in Modeling Earth Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1029/2025MS005181
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 1
    Subjects:
      – SubjectFull: Nutrient cycles
        Type: general
      – SubjectFull: Climate change
        Type: general
      – SubjectFull: Resorption (Physiology)
        Type: general
      – SubjectFull: Atmospheric models
        Type: general
      – SubjectFull: Ecosystem dynamics
        Type: general
      – SubjectFull: Environmental history
        Type: general
      – SubjectFull: Plant productivity
        Type: general
      – SubjectFull: Deciduous plants
        Type: general
    Titles:
      – TitleFull: Dynamic Nitrogen Resorption Improves Predictions of Nitrogen Cycling Responses to Global Change in a Next Generation Ecosystem Model.
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            NameFull: Sophia, Gabriela
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            NameFull: Caldararu, Silvia
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            NameFull: Stocker, Benjamin D.
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            NameFull: Zaehle, Sönke
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            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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