Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4931-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
TREED (v1.0): a trait- and optimality-based eco-evolutionary vegetation model for the deep past and the present
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- Final revised paper (published on 12 Jun 2026)
- Preprint (discussion started on 17 Dec 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2025-6002', Anonymous Referee #1, 26 Jan 2026
- AC1: 'Reply on RC1', Julian Rogger, 09 Mar 2026
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RC2: 'Comment on egusphere-2025-6002', Anonymous Referee #2, 15 Feb 2026
- AC2: 'Reply on RC2', Julian Rogger, 09 Mar 2026
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Julian Rogger on behalf of the Authors (09 Mar 2026)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (26 Mar 2026) by Yilong Wang
RR by Pam Vervoort (26 Mar 2026)
RR by Elke Zeller (11 Apr 2026)
ED: Publish as is (14 Apr 2026) by Yilong Wang
AR by Julian Rogger on behalf of the Authors (23 Apr 2026)
Manuscript
The TREED model is a vegetation model designed to simulate global vegetation dynamics and biodiversity potential outputting GPP, NPP, evapotranspiration, average plant height, below, and above ground biomass storage. These variables are simulated on an average grid point bases by applying eco-evolutionary optimality principles. The plants in the model adapt to maximize fitness under a steady state or transient climates. Evolving the plants to maximize their fitness lets TREED simulate average plant traits in the grid point without making assumptions with regards to the climate niche. This framework offers a method for understanding how biological systems reorganize during different climates.
The paper is well written and explains the technical parts of the paper in detail. The authors have taken the time to go over all the equations used in the model and describe how and why they are implemented. The paper can benefit from more Thurow evaluation to show where the limitations are, please see comments below. In addition, the authors have carefully provided a limited sensitivity study appropriate for this paper. I hope to see a follow up sensitivity study further exploring the trait evolutions of the plants in different paleo climates, but this would be too much for this paper. I have some minor comments and recommend this paper for publication:
Line 178: I think “ration” should be “ratio”
Table 1: would it be possible to add citations for the values (where applicable) or an indicator, something like “this study” for where you have made your own estimates. In addition, an indicator for the sensitivity level? I understand that this might be subjective at the moment and needs to be further investigated but as a potential user of this model that would help assess if I can/should/want/need to change this value or not.
I think “fumeroot” should be “fine root” and “Rubisco specifity” should be “Rubisco specificity”
Section 5.3: Please expand this analysis to include a more regional evaluation of where the model does well vs. where the model is lacking. In addition, the reasons given for the mismatched between the model and data are too brought and should be more mechanism specific where possible. For example, “Reduced height growth under high NPP levels indicates carbon turnover processes that are not currently represented in TREED and its height optimization.” Which processes are not represented and how does this effect the model? Is this region specific or a global problem? Do this for all three variables, NPP, GPP, and AET. This will help understand when the model is appropriate to use and the model limitations.
Figure 7: A contour plot will work better here showing the density of the points
Figure 8c: Please use a density plot here
Figure 8d: Try to see if a density plot works, it might not because of the
Figure 11: What alphas and dispersal rates are used? (Add numbers to slow, fast, and intermediate)
Section 7: It would be nice to add a section on model limitations. Several limitations are already stated in the appropriate sections however, I think the paper can benefit to list them again and address them in a more systematic way.
Section 9: Please add the GitHub tag (or release name) also in the text, this makes it easier to check out the specific version.