the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Temperature Acclimation of Photosystem II Efficiency across Plant Functional Types and Climate
Abstract. Modelling terrestrial gross primary productivity (GPP) is central to predicting the global carbon cycle. Much interest has been focused on the environmentally induced dynamics of photosystem energy partitioning and how improvements in the description of such dynamics assist the prediction of light reactions of photosynthesis and therefore GPP. The maximum quantum yield of photosystem II (ΦPSIImax) is a key parameter of the light reactions that influence the electron transport rate needed for supporting the biochemical reactions of photosynthesis. ΦPSIImax is generally treated as a constant in biochemical photosynthetic models even though a constant ΦPSIImax is expected only for non-stressed plants. We synthesized reported ΦPSIImax values from Pulse-amplitude modulated fluorometry measurements in response to variable temperatures across the globe. We found that ΦPSIImax is strongly affected by prevailing temperature regimes with declined values in both hot and cold conditions. To understand the spatiotemporal variability of ΦPSIImax, we analysed the dependence of the temperature acclimation of ΦPSIImax on plant functional type (PFT) and habitat climatology. The analysis showed that temperature acclimation of ΦPSIImax is shaped more by climate than by PFT for plants with broad latitudinal distributions or in regions with extreme temperature variability. There is a trade-off between the temperature range within which ΦPSIImax remains maximal and the overall rate of decline of ΦPSIImax outside the temperature range such that species cannot be simultaneously tolerant and resilient to extreme temperatures. Our study points to a quantitative approach for improving electron transport and photosynthetic productivity modelling under changing climates at regional and global scales.
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RC1: 'Comment on bg-2023-163', Anonymous Referee #1, 09 Jan 2024
The manuscript by Neri et al. explored how maximum PSII yield changes with PFT and climate using data collected from the literature. The research topic was of great importance for the global carbon cycle, and implementing the idea in terrestrial biosphere models will help improve the model predictions. The manuscript was overall well written, and ideas were well-delivered. While I am convinced about the importance of the idea, I have some concerns about the research and analyses performed. Below are two primary issues I found, and I hope they are useful for the authors.
- Simply modifying Phi_PSIImax is not adequate for photosynthesis and thus fluorescence models. For example, if the change of Phi_PSIImax is due to those of the rate constants, such as Kd, Kf, Kn, and Kpmax, prescribing Phi_PSIImax will only impact the calculation of electron transport rate J and thus Aj and Agross. However, the subsequent qL, NPQ, and Phi_f calculations will not be accurate as the Kd/f/n/pmax are not changing accordingly. Therefore, a more process-focused model to explain Phi_PSIImax will be more useful. For example, the van der Tol et al. (2013) fluorescence model assumed that Kd is temperature-dependent to explain the temperature dependency of Phi_f on temperature. A similar approach, such as a revised Kn(temperature) function, can be taken here.
- The authors did not distinguish “response” and “acclimation” in the analyses. For example, let us again assume Phi_PSIImax change is due to those of Kd, Kf, Kn, and Kpmax here. If Kd = a1*T + b1 for plants grown in the C1 environment and Kd = a2*T + b2 for plants grown in the C2 environment, the function a*T + b is “response” (related to temporary changes in the environment), and shift from a1*x + b1 to a2*x + b2 is “acclimation” (related to long term changes in climate). Therefore, it is likely that the data analyzed is a mixture of “response” and “acclimation”, and attributing all the changes in Phi_PSIImax is inappropriate. Without distinguishing the two, the analyses performed might be biased.
Citation: https://doi.org/10.5194/bg-2023-163-RC1 -
AC1: 'Reply on RC1', Yang Song, 06 Feb 2024
We appreciate this reviewer’s comments and careful reading of the manuscript, and the insights provided to us. We have carefully considered each question raised and will revise the manuscript accordingly. Please see our responses to each specific comment in the attachment.
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RC2: 'Comment on bg-2023-163', Anonymous Referee #2, 16 Jan 2024
Neri et al synthesized PAM measurements of ΦPSIImax from literature, and investigated its temperature responses. A model with interpretable parameters is developed, and a tolerance-resilience trade-off is identified. The impacts of PFT and climatological temperature on ΦPSIImax tolerance and resilience are also investigated. While plenty ΦPSIImax measurements can be found in literature, a synthesis analysis as this work is absent. The presented work could be valuable to the community by facilitating our understanding of photosynthesis temperature response, and providing information for model parameterizations. The manuscript effectively presented the methods and results in general. Below lists my several concerns and suggestions.
- It is not clear to me how the confounding variables (water, light, etc.) were controlled, although that is stated as a selection criterion (L125). Did you select studies where the confounding variables were controlled in that specific study? My understanding is those variables can still vary from one study to another, and may play a role in the analyses. Could you clarify this?
- PFT-specific CTI and percent prediction explained (Eq.7 and Figure 5): My understanding is that the PFT-specific CTI is still one equation generated for all PFTs, rather than one equation for each PFT. Is this correct? Could you explain the reason for using a general equation instead of one equation for each PFT? Presenting the values of the aL parameters might also be helpful.
- Rearranging Section 3.2 and Section 3.3 and putting the CTI map (Figure 8c) before Figures 6 and 7 may help the audience more easily interpret results related to CTI.
- Are the CTIs in the result section the general CTI?
- The manuscript is quite long, I suggest cutting the length of the manuscript. Some method and result sections could potentially be moved to the supplementary. For example, details of ART ANOVA, section 3.2.1, and section 3.2.3.
Citation: https://doi.org/10.5194/bg-2023-163-RC2 -
AC2: 'Reply on RC2', Yang Song, 06 Feb 2024
We thank this referee for appreciating our work and for such a thorough reading of the manuscript. The comments and suggestions made in this review are very helpful in guiding us to improve the text of our manuscript. We have responded to each point as detailed in the attachment.
Status: closed
-
RC1: 'Comment on bg-2023-163', Anonymous Referee #1, 09 Jan 2024
The manuscript by Neri et al. explored how maximum PSII yield changes with PFT and climate using data collected from the literature. The research topic was of great importance for the global carbon cycle, and implementing the idea in terrestrial biosphere models will help improve the model predictions. The manuscript was overall well written, and ideas were well-delivered. While I am convinced about the importance of the idea, I have some concerns about the research and analyses performed. Below are two primary issues I found, and I hope they are useful for the authors.
- Simply modifying Phi_PSIImax is not adequate for photosynthesis and thus fluorescence models. For example, if the change of Phi_PSIImax is due to those of the rate constants, such as Kd, Kf, Kn, and Kpmax, prescribing Phi_PSIImax will only impact the calculation of electron transport rate J and thus Aj and Agross. However, the subsequent qL, NPQ, and Phi_f calculations will not be accurate as the Kd/f/n/pmax are not changing accordingly. Therefore, a more process-focused model to explain Phi_PSIImax will be more useful. For example, the van der Tol et al. (2013) fluorescence model assumed that Kd is temperature-dependent to explain the temperature dependency of Phi_f on temperature. A similar approach, such as a revised Kn(temperature) function, can be taken here.
- The authors did not distinguish “response” and “acclimation” in the analyses. For example, let us again assume Phi_PSIImax change is due to those of Kd, Kf, Kn, and Kpmax here. If Kd = a1*T + b1 for plants grown in the C1 environment and Kd = a2*T + b2 for plants grown in the C2 environment, the function a*T + b is “response” (related to temporary changes in the environment), and shift from a1*x + b1 to a2*x + b2 is “acclimation” (related to long term changes in climate). Therefore, it is likely that the data analyzed is a mixture of “response” and “acclimation”, and attributing all the changes in Phi_PSIImax is inappropriate. Without distinguishing the two, the analyses performed might be biased.
Citation: https://doi.org/10.5194/bg-2023-163-RC1 -
AC1: 'Reply on RC1', Yang Song, 06 Feb 2024
We appreciate this reviewer’s comments and careful reading of the manuscript, and the insights provided to us. We have carefully considered each question raised and will revise the manuscript accordingly. Please see our responses to each specific comment in the attachment.
-
RC2: 'Comment on bg-2023-163', Anonymous Referee #2, 16 Jan 2024
Neri et al synthesized PAM measurements of ΦPSIImax from literature, and investigated its temperature responses. A model with interpretable parameters is developed, and a tolerance-resilience trade-off is identified. The impacts of PFT and climatological temperature on ΦPSIImax tolerance and resilience are also investigated. While plenty ΦPSIImax measurements can be found in literature, a synthesis analysis as this work is absent. The presented work could be valuable to the community by facilitating our understanding of photosynthesis temperature response, and providing information for model parameterizations. The manuscript effectively presented the methods and results in general. Below lists my several concerns and suggestions.
- It is not clear to me how the confounding variables (water, light, etc.) were controlled, although that is stated as a selection criterion (L125). Did you select studies where the confounding variables were controlled in that specific study? My understanding is those variables can still vary from one study to another, and may play a role in the analyses. Could you clarify this?
- PFT-specific CTI and percent prediction explained (Eq.7 and Figure 5): My understanding is that the PFT-specific CTI is still one equation generated for all PFTs, rather than one equation for each PFT. Is this correct? Could you explain the reason for using a general equation instead of one equation for each PFT? Presenting the values of the aL parameters might also be helpful.
- Rearranging Section 3.2 and Section 3.3 and putting the CTI map (Figure 8c) before Figures 6 and 7 may help the audience more easily interpret results related to CTI.
- Are the CTIs in the result section the general CTI?
- The manuscript is quite long, I suggest cutting the length of the manuscript. Some method and result sections could potentially be moved to the supplementary. For example, details of ART ANOVA, section 3.2.1, and section 3.2.3.
Citation: https://doi.org/10.5194/bg-2023-163-RC2 -
AC2: 'Reply on RC2', Yang Song, 06 Feb 2024
We thank this referee for appreciating our work and for such a thorough reading of the manuscript. The comments and suggestions made in this review are very helpful in guiding us to improve the text of our manuscript. We have responded to each point as detailed in the attachment.
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