Allometric Models to Estimate Aboveground Biomass of Individual Trees of Eucalyptus saligna Sm in Young Plantations in Ecuador

Response to Reviewer 1 Comments

Major comements

This study develops an allometric model for estimating the aboveground biomass of Eucalyptus saligna plantations in Ecuador’s Lower Montane thorny steppe using diameter at breast height (dbh) as the sole predictor. The research addresses a critical gap in species-specific and localized biomass estimation tools, offering significant scientific and practical value for climate change mitigation efforts. The methodology is systematic, the data collection and analysis processes are transparent, and the model validation is rigorous. The paper is well-structured, logically coherent, and the conclusions align with the results. However, certain methodological details, discussions on model applicability, and language clarity require refinement to enhance the study’s depth and generalizability.

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions.

Point-by-point Response to Comments and Suggestions for Authors

Comments 1: While the sample size (46 trees) meets basic statistical requirements, further elaboration is needed on the randomization of sample selection (e.g., coverage across planting densities, soil microenvironments). Discuss how sample size impacts the model’s extrapolation capacity. Additionally, clarify whether all sampled trees were uniformly six years old and analyze the influence of tree age on biomass allocation.

Response 1: We agree with this comment. We improve the details in selection of trees.  When the sample is larger than 30 individuals covering all population sizes, the extrapolation or applicability of the model is assured. All trees were six years old, so it is not possible to study the impact of age on biomass allocation.

Comments 2: Although normality was confirmed for dbh, Bs, and Bt via the Shapiro-Wilk test, Br and Bl failed normality. Consider supplementing results with non-parametric models (e.g., quantile regression) or justifying the choice of linear regression. Explicitly report Breusch-Pagan test results (p-values) for heteroscedasticity in logarithmic models (currently stated as a range of 0.05–0.90).

Response 2: Although Bb and Bl were not normal, their transformations were normal (p>0.05). This was included in the text. Many authors claim that logarithmic transformation is one of the techniques that corrects the lack of normality of the data. Reviewer 3 agrees with that technique. Data for Breusch-Pagan test results were included in Table 2.

Comments 3: The statement “CF did not improve model performance” lacks detailed justification. Provide the mathematical definition of CF and quantitative comparisons (e.g., MRE and ME values with/without CF) to clarify why CF was omitted.

Response 3: Definition of CF used in the paper is that proposed by three papers, which were included in the M&M section and references. A table with values of MRE and ME for models with/without CF was included.

Comments 4: When comparing results with Senelwa & Sims (1998) and Momolli et al. (2019), discuss potential error sources (e.g., climatic differences, silvicultural practices, measurement protocols). For instance, could Ecuador’s high-altitude conditions (e.g., lower temperatures) explain underestimation by generic models? Integrate ecophysiological mechanisms into this analysis.

Response 4: We agree. We added a sentence discussing this.

Comments 5: The Biomass Expansion Factor (BEF = 1.6) and its increase in smaller trees warrant deeper discussion. Explore how BEF variability affects carbon stock estimates and interpret leaf/biomass ratios (29–35%) in the context of canopy adaptation strategies (e.g., light competition).

Response 5: It was added in discussion. However, this paper developed biomass models, which are not considering adaptations.

Comments 6: Define the model’s scope explicitly (e.g., applicability to 6-year-old plantations, specific soil types, or management regimes). Discuss its robustness under extreme climatic events (drought, pests) and suitability for mixed-species or natural forests.

Response 6: These models and BEF are applicable to this area and other regions with similar conditions. These models are for trees with dimensions between the range of the tree sampled, these are not models for specific 6-year-old trees. These models are not applicable to extreme conditions or mixed and natural forests. These models can be validated to define if they can be used in other conditions. This is mentioned in the discussion.

Comments 7: Figure 2 (correlation heatmap): Add significance markers (e.g., asterisks for p-values).

Response 7: We have improved the figure.

Comments 8: Figure 3 (biomass component relationships): Standardize axis labels (some subplots lack units).  

Response 8: The axis labels are standardized; this variable can be dimensionless or kg/kg. In the text, we prefer to use these data with no units.

Comments 9: Table 2 (model statistics): Align numerical values (AIC, BIC) and specify whether they are raw or log-transformed.

Response 9: We have incorporated all the suggested corrections in this table.

Comments 10: Replace outdated citations (e.g., Senelwa & Sims, 1998; Boland et al., 1984) with recent studies (e.g., advances in tropical plantation biomass modeling from the past 5 years) to reflect current research trends.

Response 10: We disagree with changing these two references, although they are not recent. This is because the first one is from the model with which it was compared and the second one specifies the optimum growth conditions for this species.

Comments 11: Replace uncommon terms like “dasometric variables” with standard alternatives (e.g., “dendrometric variables”). Define abbreviations at first mention (e.g., BEF: Biomass Expansion Factor).

Response 11: Done.

Comments 12: Strengthen the introduction by linking “Nature-based Solutions (NbS)” to Ecuador’s specific forestry policies or case studies. This contextualization will enhance the relevance of the research background.

Response 12: This was included in introduction.

Response to Reviewer 2 Comments

Major comements

The aim of this thesis was to develop an anisotropic growth model to estimate the aboveground biomass of Eucalyptus multispinosa grasslands in the lowland mountains of Ecuador using a combination of destructive and non-destructive methods. Sixteen models were tested in this study and a best-fit model based on diameter at breast height (dbh) was proposed. Although this study fills an important gap in the estimation of forestry biomass in Ecuador, there are still several areas that need to be improved, especially in terms of language clarity, data presentation, and methodological interpretation.

Significance of this paper
Filling a research gap: This study provides a much-needed biomass estimation model for eucalypts in Ecuador, which is crucial for forest management and carbon sequestration.
Practicality: The proposed model relies only on dbh, which is easy to measure and widely available in forest inventories. This simplicity enhances its applicability.
Comparative analysis: The study compares local models with existing literature and demonstrates the superiority of region-specific models in terms of accuracy.
Specific comments and recommendations.

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions.

Point-by-point Response to Comments and Suggestions for Authors

Comments 1: some sentences are grammatically incorrect or poorly structured, affecting readability. Thorough language editing was performed, especially in Section 3 (Results) and Section 4 (Discussion).

Response 1: We agree with this comment. Therefore, in this version, we include a review of a native English language.

Comments 2: The authors do not clearly explain how to select the best model from the 16 candidate models. Evaluation metrics for all models are not provided. In the Results section, provide a detailed table with the evaluation metrics (e.g., R2, RMSE, AIC, BIC) for all 16 models.

Response 2: We agree. This explaination was included in the manuscript. We disagree with the second part of this comment. This is due to we tested 16 * 4 models, so to include the metrics of these 64 models is not practical.

Comments 3: the data was collected in 2018, but the paper was published much later. The timeliness and persuasiveness of the data is questionable. The paper does not mention whether new data was used to validate the model.

In the discussion section, explain why the data collection was conducted in 2018 and address any potential limitations related to the timeliness of the data.

Provide information on whether new data was used to validate the model in recent years. If so, provide the results of the validation. If not, suggest plans for future validation using newer data.

Response 3: We consider that the time of data collection is not so important in this type of study, since we are basically looking for the mathematical relationship between tree variables, which does not vary greatly in a few years. We agree with the second part of the comment, we included it in the discussion.

Response to Reviewer 3 Comments

Major comements

The study aimed to develop allometric models for the biomass prediction of Eucalyptus saligna young plantations in Ecuador.  I find the study interesting, well-structured, and well-communicated. However, I have focused on some significant flaws that are subject to major revisions. My comments are provided in the following lines.

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions.

Point-by-point Response to Comments and Suggestions for Authors

Comments 1: L105-108 I suggest improving the last paragraph of the introduction section, by clearly stating the basic and the secondary aims of the current study.

Response 1: We agree with this comment. We included your suggestion. 

Comments 2: Figure 1. I recommend increasing the figure’s dimensions to provide a more detailed depiction of its elements.

Response 2: We are suggesting this to the journal editor.

Comments 3: L154 I recommend replacing “dasometric” with “tree” term throughout the ms.

Response 3: We changed dasometric by dendrometric as suggestion of other reviewer.

Comments 4: L155 Perhaps the height of the first branch is better described with “height to live crown” or “crown base height” in forest science.

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Response 4: We agree with this comment. We include your suggestion. 

Comments 5: L212-214 I think that the CF uses the MSE and not the RMSE. Please, revise and the results also. In addition, please add the relevant citation for the CF (Baskerville 1972).

Response 5: Thank you for pointing this out. We checked definition and calculation of CF and you are right, thank you. We corrected that in the equation and in the calculations. We included the reference you proposed: thanks.

Comments 6: L232 and figure 2. If the normality assumption of the Bl is not met, why the Pearson’s correlation was used? Please explain. I suggest checking the other variables also.  

Response 6: Thank you for pointing this out. We used the transformation of this variables, which are normal.

Comments 7: Table 2. First of all, I agree with the logarithmic transformation of the proposed models. It is a valid way for biomass modeling. However, I suggest improving the table 2:

-Please add the SE of the model’s parameter estimates.

-At this stage, the AIC, the BIC, and the FI are meaningless, since they have been used in a previous stage for model comparison. I suggest adding the MSE for the CF estimation.

Response 7: Thank you for pointing this out. We include your suggestions in the table 2

Comments 8: L290 Please correct the parenthesis.

Response 8: Done

Comments 9: L273-275 Please see the 5^th comment and revise accordingly.

Response 9: Done.

Comments 10: Figure 4 and 5. These figures present significant flaws. The observed values should be placed on the y-axis, otherwise it leads to an erroneous estimate of the slope and intercept (please see 10.1016/j.ecolmodel.2008.05.006). In addition, the estimated line between the observed and predicted values should have an intercept not significantly different from zero and a slope not significantly different from one. Please revise.

Response 10: We changed the axis as you recommended. However, this graph is just for comparing this model with others. Figures 4 and 5 were the same in the previous due to a mistake. We eliminated one of them.

Comments 11: The discussion should be revised, according to the new findings.

Response 11: Done

Response to Reviewer 3 Comments

Major comements

The authors have made all the suggested changes, improving the manuscript significantly.

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions.

Point-by-point Response to Comments and Suggestions for Authors

Comments 1: L374-376 Please check the statement. Please note that a CF is currently proposed for all models.

Response 1: We agree with this comment. We have made your suggestion.