the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Dec 2025
TREED (v1.0): a trait- and optimality-based eco-evolutionary vegetation model for the deep past and the present
Abstract. We present the TREED model (TRait Ecology and Evolution over Deep time), a trait- and optimality-based vegetation model to simulate vegetation structure, carbon cycling and eco-evolutionary adaptation dynamics to climate and CO2 changes across geologic time scales. The global grid-based vegetation model represents plant carbon allocation and trait evolution as a set of carbon economic trade-offs. Based on optimality principles, it is assumed that functional traits of the modelled community-representative average plants evolve towards an optimum that maximizes height growth while maintaining a positive carbon balance. The considered trait trade-offs resolve the potential plant height, leaf carbon pool size, leaf longevity, and phenology as the major axes of plant trait variation. Based on these key traits, whole-plant structure and functioning are derived using functional and allometric relationships. In its eco-evolutionary mode, vegetation-mediated carbon cycling can be tracked over the course of climatic transitions, testing the effects of the speed of evolutionary trait adaptation and dispersal dynamics. Moreover, with its generalized plant physiology, continuous trait space, and lack of pre-defined functional types, the model can be used to calculate metrics of biodiversity, including indices of the functional diversity and species richness potential. With a low computational demand, a flexible time stepping scheme and scalable adaptation parameters, TREED is intended to simulate biological and environmental transitions across time scales spanning from centuries to millions of years. Here, we present the underlying theory and model functions and evaluate model outputs against present-day observations. We show that the trait- and optimality-based approach captures major patterns in present-day vegetation-mediated carbon and water fluxes, biomass carbon storage, vegetation height, leaf traits, as well as the global distribution of plant biodiversity. Finally, we illustrate its application in the context of paleoclimate and palaeoecological research using the Paleocene-Eocene Thermal Maximum as a case study and show how eco-evolutionary adaptation dynamics of terrestrial ecosystems may strongly affect global carbon cycle dynamics during hyperthermal events. The TREED model is a step towards a more self-consistent and parameter-scarce representation of vegetation dynamics under environmental conditions that are fundamentally different from the present. In combination with geochemical and paleobotanical data, the model may help to better constrain the resilience of vegetation-mediated Earth system functions to perturbations in the geologic past and at present.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-6002', Anonymous Referee #1, 26 Jan 2026
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RC2: 'Comment on egusphere-2025-6002', Anonymous Referee #2, 15 Feb 2026
The manuscript ‘TREED (v1.0): a trait- and optimality-based eco-evolutionary vegetation model for the deep past and the present’ by Rogger et al. presents a description of a numerical, grid-based model to simulate trait-based vegetation dynamics and associated carbon-water fluxes, either in steady-state or transiently in response to environmental perturbation. The majority of the manuscript outlines the model development hence is suitable for publication in GMD. The model performance is evaluated by comparing the modelled carbon fluxes and vegetation traits in a modern climate to observations. Finally, the authors provide examples to demonstrate how the model can be used for paleo applications.
A major advantage of this trait-based model compared to models that simulate many different plant functional types is that it computationally cheap and circumvents the need to make assumptions about vegetation types in paleo-settings that are a large source of uncertainty. This makes the TREED model very suitable for paleoclimate studies. The new implementation of adaptation timescales and rate of evolution makes it especially interesting to study transient global-scale climate events of the past, both in terms of carbon cycle disruption and feedbacks at the onset of an event and also the expected timescales of recovery. Overall, the manuscript is well structured and describes all stages of the model clearly. Readers can easily identify the applied relationships in a transparent way and implemented equations are backed up with sufficient references for traceability. The model output compares well to present-day observations, demonstrating accurate simulation of carbon and water fluxes as well as key traits. Based on the above and added paleoclimate application, I strongly recommend publication in GMD. I only have a few comments or suggestions that could improve clarity in some areas.
General comments/suggestions
Caption Figure 1. A model schematic in my eyes is very important to show model mechanics. Please expand description, e.g. describe difference between square boxes and circles, and which components are model inputs versus outputs. Perhaps in the figure or caption also note time stepping of each component, e.g. is a calculation or action done once, or iteratively as the model runs through time/months and how does this differ between steady state and transient simulations? Also, the ‘adaptation’ step is optional if I’m correct and is only needed in transient runs. You could clarify the alternative pathway for steady-state simulations by connecting the ‘optimisation’ and ‘key traits’ boxes. (I only noticed Figure 3 further down the text. Might be worth combining Figure 1 and 3 into one that describes the whole model structure, inputs versus output, order of actions, and time stepping. Also clarify that -presumably- the ‘1 year of monthly climate inputs’ is updated to new fields in step 2).
The evaluation against present-day observations in Section 5 demonstrates the model performance. However, from sections above it is not entirely clear if or how model tuning has been performed beyond “All allometric constants were calibrated using present-day canopy height and above ground biomass data”. I’d like to know e.g. which parameter values are inherited from previous studies, and which parameters have been tested to find the best match to present-day fields. If tuning was performed, please indicate what evaluation metric(s) have been used to find the best fit and what model vs data fields are compared. Might be resolved by adding an extra column to Table 1 that shows ‘source’ or parameter value and/or whether it’s tuned in this model development and how.
The PETM application is a nice demonstration of how the model can be used for paleo simulations. Considering this is a model development paper, I’d be very interested to see a more extended evaluation of model mechanics that drive the patterns in Figs 10 and 11. It would be a good opportunity to show the effects of e.g. the biotic stress components in the context of climatic niches (Eq.52-55). Any geographical regions that are particularly water- or heat-stressed given certain evolutionary adaptation potentials and rates of dispersal?
For review, I also attempted to run the TREED model but climate input fields are currently not uploaded so I could not verify if the model runs okay or whether results as reported in the manuscript are reproduced. I can, however, confirm that the uploaded code is accessible and includes a readme with sufficient information to run the model.
Line-by-line comments:
- Line 69-70. “…are fundamentally different from the present, as is the case for most of Earth’s past (Judd et al., 2024), or as we expect …”
- Figure 2. Instead of ‘Model’ in black, use TREED for clarity.
- Line 231-232. Needs reference to support 50% of downward SW radiation is photosynthetically active radiation.
- Line 286-288. Add reference for equation 30.
- Line 329 “…using a modified Arrhenius equation that accounts for declining respiration rates with temperatures”. Should it be ‘increasing respiration rates with temperature’? Equation 39 shows a positive relation between temperature and g.
- Figure 4 and 6. Can you add column titles saying ‘TREED’ and ‘Observation’ for clarity?
- Figure 8 caption. (c) Modelled latitudinal distribution…
- Figure 11 caption. Define the ‘alpha’ again as you did for ‘k’
- Line 746. Add a few sentences to clarify how the different scenarios are simulated? Have you used new climate field inputs that correspond to 2, 4, 6, 8°C?
Citation: https://doi.org/10.5194/egusphere-2025-6002-RC2
Model code and software
Code repository Julian Rogger https://github.com/julrogger/TREED
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- 1
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.