Optimal Planting Areas of Sea Buckthorn (Hippophae rhamnoides) Under the Influences of Climate Change and Pests Using the MaxEnt Model

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors According to the authors' research, by constructing a synergistic niche model for sea buckthorn and its two major pests, this study identified the optimal planting area with ecological suitability and pest prevention potential in a multiclimatic environment, thereby achieving a highly accurate prediction of the dual objectives of suitability and prevention potential in China. In the manuscript "Agronomía-3872061," it was reported that the findings can strengthen the decision-support function of the model and build a new interregional, multi-scale, climate-adapted planting zoning system, providing a solid scientific foundation for the stable development of the sea buckthorn industry in China. Judging from the attached figures, this is a novel research that contributes to scientific knowledge. The rigorous application of the scientific method is also evident, confirming novel results that contribute to the development and potential of a problematic plant species and pests. The manuscript is concise and the grammar is basically correct. It is hoped that the research paper, "Evaluation of optimal planting areas of sea buckthorn (Hippophae rhamnoides L.) under the influence of climate change and pests using the MaxEnt model," will attract sufficient attention and be of great significance to the development of your country. Before publication, we suggest considering the following questions and suggestions and carefully reviewing the manuscript. Suggestions: 1. Carefully review the percentages; these are indicated in the manuscript. 2. Comment on the supplementary figures. In this regard, the figures are of high quality; however, it is suggested that the geographic coordinates of the specific location(s) where the research was conducted or the area of ​​influence from which the reference points were taken be included in the materials and methods section.

Comments for author File: Comments.pdf

Author Response

Comments: According to the authors' research, by constructing a synergistic niche model for sea buckthorn and its two major pests, this study identified the optimal planting area with ecological suitability and pest prevention potential in a multiclimatic environment, thereby achieving a highly accurate prediction of the dual objectives of suitability and prevention potential in China. In the manuscript "Agronomía-3872061," it was reported that the findings can strengthen the decision-support function of the model and build a new interregional, multi-scale, climate-adapted planting zoning system, providing a solid scientific foundation for the stable development of the sea buckthorn industry in China. Judging from the attached figures, this is a novel research that contributes to scientific knowledge. The rigorous application of the scientific method is also evident, confirming novel results that contribute to the development and potential of a problematic plant species and pests. The manuscript is concise and the grammar is basically correct. It is hoped that the research paper, "Evaluation of optimal planting areas of sea buckthorn (Hippophae rhamnoides L.) under the influence of climate change and pests using the MaxEnt model," will attract sufficient attention and be of great significance to the development of your country. Before publication, we suggest considering the following questions and suggestions and carefully reviewing the manuscript. Suggestions: 1. Carefully review the percentages; these are indicated in the manuscript. 2. Comment on the supplementary figures. In this regard, the figures are of high quality; however, it is suggested that the geographic coordinates of the specific location(s) where the research was conducted or the area of influence from which the reference points were taken be included in the materials and methods section.

Response: We sincerely thank the reviewer for their thoughtful comments, which have greatly helped us improve the manuscript. In response to the suggestions regarding data accuracy, we have conducted a thorough re-examination of all percentage values presented in the text. All specific percentage figures have now been standardized to two decimal places to enhance precision—for example, “approximately 62%” in the abstract has been revised to “61.95%.” In cases where descriptive approximations are more appropriate, we have used phrasing such as “approximately” without citing exact values. During this process, we identified and corrected an error in Section 3.3, where the retention rate of the area severely affected by pests under the SSP585-2090s scenario was inaccurately reported as 64.77%; this has been amended to 9.59% based on the correct calculation from Table 6. In addition, integrating suggestions from this reviewer and another, we have expanded the supplementary materials to include a detailed table of geographical coordinates for the species occurrence points used in modeling, along with a full set of high-resolution maps showing Multivariate Environmental Similarity Surface (MESS) and Most Dissimilar Variable (MoD) results across all future climate scenarios. These additions provide greater transparency and contextual depth to our study. We deeply appreciate the reviewer's careful reading and valuable input, which have undoubtedly strengthened the quality of our paper.

Reviewer 2 Report

Comments and Suggestions for Authors

This study explores the potential distribution of Hippophae rhamnoides and two associated pest species under current and future climate scenarios using the MaxEnt modeling framework. The research topic is timely and potentially valuable for guiding ecological management, but the manuscript contains several major weaknesses in data handling, modeling design, result interpretation, and overall scientific communication. These issues must be addressed before the study can be considered for publication. First, the most critical concern is the lack of data transparency. The manuscript does not provide the geographic coordinates of the occurrence records used for model calibration. This severely limits reproducibility. For the pest species, the locations were derived from textual sources and converted via an online mapping tool, yet no evidence is given to confirm the spatial accuracy of these points. There is no mention of specimen images, validation with GBIF IDs, or photographic confirmation. Second, the sample sizes for pest species are relatively small, while the number of environmental predictors used in the final model remains high at 15. Although the authors applied multicollinearity filtering, this predictor-to-sample ratio increases the risk of overfitting. No variable contribution ranking or jackknife test results are provided to support the inclusion of these variables. Third, the binary habitat suitability maps are generated using the maximum sensitivity plus specificity threshold method, which is an appropriate approach. However, the actual threshold values used for each species are not reported. This omission compromises transparency and makes it difficult to interpret the binary classifications or compare model outputs across species. Fourth, the future climate projections modify only the climate variables while keeping the soil and topographic layers unchanged. This inconsistency should be explicitly acknowledged, and its implications for model accuracy and transferability should be discussed. Projecting models into novel environmental spaces requires careful justification, which is not currently provided. Fifth, there is no clear treatment of sampling bias. The authors mention spatial deduplication using a 5 km grid, but no spatial thinning or background bias correction layer appears to have been used. Given the uneven reporting likelihood in government pest surveys, this is a potentially serious source of bias that remains unaddressed. Sixth, the manuscript does not include any uncertainty estimates for model predictions. No standard deviation maps, clamping layers, or MESS/MOP analyses are presented. Without these elements, the robustness of projections into future scenarios cannot be assessed. Lastly, the manuscript would benefit from improved writing clarity and stronger engagement with recent literature. Some descriptions are vague or overly general, and the references cited are heavily weighted toward older studies. Key methodological literature in species distribution modeling published in the past five years should be incorporated. In conclusion, while the general research direction is meaningful, the current version of the manuscript falls short in data transparency, methodological rigor, and scientific presentation. Substantial revisions are needed to improve the credibility and utility of the findings.

Author Response

This study explores the potential distribution of Hippophae rhamnoides and two associated pest species under current and future climate scenarios using the MaxEnt modeling framework. The research topic is timely and potentially valuable for guiding ecological management, but the manuscript contains several major weaknesses in data handling, modeling design, result interpretation, and overall scientific communication. These issues must be addressed before the study can be considered for publication. First, the most critical concern is the lack of data transparency. The manuscript does not provide the geographic coordinates of the occurrence records used for model calibration. This severely limits reproducibility. For the pest species, the locations were derived from textual sources and converted via an online mapping tool, yet no evidence is given to confirm the spatial accuracy of these points. There is no mention of specimen images, validation with GBIF IDs, or photographic confirmation. Second, the sample sizes for pest species are relatively small, while the number of environmental predictors used in the final model remains high at 15. Although the authors applied multicollinearity filtering, this predictor-to-sample ratio increases the risk of overfitting. No variable contribution ranking or jackknife test results are provided to support the inclusion of these variables. Third, the binary habitat suitability maps are generated using the maximum sensitivity plus specificity threshold method, which is an appropriate approach. However, the actual threshold values used for each species are not reported. This omission compromises transparency and makes it difficult to interpret the binary classifications or compare model outputs across species. Fourth, the future climate projections modify only the climate variables while keeping the soil and topographic layers unchanged. This inconsistency should be explicitly acknowledged, and its implications for model accuracy and transferability should be discussed. Projecting models into novel environmental spaces requires careful justification, which is not currently provided. Fifth, there is no clear treatment of sampling bias. The authors mention spatial deduplication using a 5 km grid, but no spatial thinning or background bias correction layer appears to have been used. Given the uneven reporting likelihood in government pest surveys, this is a potentially serious source of bias that remains unaddressed. Sixth, the manuscript does not include any uncertainty estimates for model predictions. No standard deviation maps, clamping layers, or MESS/MOP analyses are presented. Without these elements, the robustness of projections into future scenarios cannot be assessed. Lastly, the manuscript would benefit from improved writing clarity and stronger engagement with recent literature. Some descriptions are vague or overly general, and the references cited are heavily weighted toward older studies. Key methodological literature in species distribution modeling published in the past five years should be incorporated. In conclusion, while the general research direction is meaningful, the current version of the manuscript falls short in data transparency, methodological rigor, and scientific presentation. Substantial revisions are needed to improve the credibility and utility of the findings.

Response: We sincerely thank you for your valuable comments and suggestions on our manuscript. These insights have been crucial in enhancing the scientific rigor, clarity, and reproducibility of our work. Below, we provide a point-by-point response to the specific issues you raised, detailing the revisions made to the manuscript .

 

Comment 1: The most critical concern is the lack of data transparency.

Response: We agree that data transparency is essential for reproducibility. In response, we have included the species occurrence data for the two pest species collected within China, as well as the Hippophae rhamnoides data downloaded and cross-verified from GBIF and CVH, as part of the Supplementary Materials. The direct links to access the H. rhamnoides data on GBIF have also been provided in the Supplementary Materials and the Reference section.

 

Comment 2: The sample sizes for pest species are relatively small.

Response: We acknowledge this point. In our study, the two pest species were modeled using 118 and 109 occurrence points, respectively. To further assess model robustness despite the sample size, we have now included the results of the Jackknife test for these pests in the Supplementary Materials. The percent contributions of the environmental variables are already provided in Section 2.2 of the main text.

 

Comment 3: The binary habitat suitability maps are generated using the maximum sensitivity plus specificity threshold method. However, the actual threshold values used for each species are not reported.

Response: Thank you for highlighting this omission. The specific threshold (cutoff) values applied for binary classification were: Hippophae rhamnoides: 0.350; Rhagoletis batava: 0.318; Cossus cossus: 0.349. These values have now been clearly indicated in the caption of Figure 3 (now the first figure presenting habitat suitability predictions).

 

Comment 4: The future climate projections modify only the climate variables while keeping the soil and topographic layers unchanged. This inconsistency should be explicitly acknowledged...

Response: We agree that this is an important methodological consideration. We have added a statement in the Methods section acknowledging this approach. It notes that reliable, high-resolution future soil data projections matching the SSP scenarios are currently lacking. Furthermore, it explains that topography and soil properties can be considered relatively constant over the century-scale of our projections. This approach of holding these variables constant is presented as a common and practical method in species distribution modeling to isolate the potential impact of climate change.

 

Comment 5: There is no clear treatment of sampling bias.

Response: To address potential sampling bias, a bias file was incorporated during model calibration using functions from the r package. The following key steps were implemented in R:pres_vals <- extract(env_stack, presence)

bg_vals <- extract(env_stack, background)

bias_index <- apply(pres_vals, 2, function(x) {

  pres_density <- density(x, na.rm=TRUE)

  bg_density <- density(bg_vals[,colnames(x)], na.rm=TRUE)

  pres_density$y / bg_density$y

})

This method helps to correct for uneven sampling effort. A detailed description has been added to the Methods section.

 

Comment 6: The manuscript does not include any uncertainty estimates for model predictions.

Response: We have now included several analyses to address model uncertainty. First, the model's performance was evaluated against a null model. As shown in Figure 2 (Section 3.1), the species model's AUC.val was significantly higher, and its OR.10p value was significantly lower than those of the null model, indicating robust predictive performance. Second, the high Continuous Boyce Index (CBI) value of 0.996 confirms the reliability of the habitat suitability predictions. Finally, to assess uncertainty in future projections, we conducted MESS (Multivariate Environmental Similarity Surface) and MoD (Most Dissimilar Variable) analyses, the results of which are presented and discussed in Section 3.4.

 

Comment 7: The manuscript would benefit from improved writing clarity and stronger engagement with recent literature.

Response: We thank you for this suggestion. The manuscript has been thoroughly edited to improve clarity. Furthermore, we have reviewed and updated the references, particularly in the Methods and Discussion sections, replacing outdated citations with relevant literature published within the last five years.

 

Once again, we extend our sincere gratitude for your time and insightful comments, which have substantially improved the quality of our manuscript. We hope our responses and revisions are now satisfactory.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

These are my main comments on the manuscript (agronomy-2872061) entitled “Assessment of the Optimal Planting Areas of Sea Buckthorn (Hippophae rhamnoides L.) Under the Influences of Climate Change and Pests using the MaxEnt Model”. The manuscript evaluates the potentially suitable planting areas for H. rhamnoides under current and future climate conditions while accounting for pest risks, and results can inform future policy and management decisions. Following substantial revisions should be incorporated in the manuscript prior to acceptance.

Title: Rephase

L.2: Delete “Assessment of the”

L.3: Delete “L.”

Abstract: The results of this study are unclear and lack sufficient detail.

L.18: Delete “approximately”

Ls.27-28: Keywords should be in alphabetic order. Also, keywords serve to widen the opportunity to be retrieved from a database. To put words that already are into title and abstracts makes KW not useful. Please choose terms that are neither in the title nor in abstract.

Introduction: A deeper understanding of the biology and ecology of both the host plant and its associated insect pests is required.

Ls.33-35: Summarize and combine these sentences.

Ls.86-95: A hypothesis for this study is needed. Furthermore, summarizes the objectives as a single objective.

Results: The results should be summarized in all their lines (i.e.: delete “In terms of the retention of areas with severe pest infestations,” see line 284).

Ls.200-205: This information should be in materials and methods section.

Discussion: The authors fail to contextualize their findings within the framework of existing research on other pest species.

L.395: Delete “In resume,”

Ls.395-402: Given the extent of the analysis presented, the conclusion warrants further development.

Author Response

These are my main comments on the manuscript (agronomy-2872061) entitled “Assessment of the Optimal Planting Areas of Sea Buckthorn (Hippophae rhamnoides L.) Under the Influences of Climate Change and Pests using the MaxEnt Model”. The manuscript evaluates the potentially suitable planting areas for H. rhamnoides under current and future climate conditions while accounting for pest risks, and results can inform future policy and management decisions. Following substantial revisions should be incorporated in the manuscript prior to acceptance.

Response: We sincerely thank Reviewer 3 for their thoughtful evaluation of our manuscript and for the constructive comments, which have been invaluable in helping us improve the quality and clarity of our work. We have carefully considered each point raised and have revised the manuscript accordingly. Our point-by-point responses are detailed below.

Title: Rephase

L.2: Delete “Assessment of the”

L.3: Delete “L.”

Response: We agree with the reviewer's suggestion for a more concise title. We have deleted "Assessment of the" and the taxonomic authority "L." as recommended.

Abstract: The results of this study are unclear and lack sufficient detail. L.18: Delete “approximately”

Response: We thank the reviewer for this suggestion. We have revised the results section in the abstract to include more specific quantitative findings to enhance clarity. We have also removed the word "approximately" and used the precise percentage.

Ls.27-28: Keywords should be in alphabetic order. Also, keywords serve to widen the opportunity to be retrieved from a database. To put words that already are into title and abstracts makes KW not useful. Please choose terms that are neither in the title nor in abstract.

Response: We appreciate this important point. We have re-selected the keywords to include terms not present in the title or abstract, and have arranged them in alphabetical order.

Introduction: A deeper understanding of the biology and ecology of both the host plant and its associated insect pests is required.

Response: We thank the reviewer for this suggestion. We have enhanced the introduction by adding key biological traits of the pests and ecological mechanisms underlying their impact. This provides a deeper rationale for our modeling approach.

Ls.33-35: Summarize and combine these sentences.

Response: We have added a brief description of the ecology of the two major pests, R. batavaand C. cossus, in the introduction. The indicated sentences have been combined and summarized for conciseness.

Ls.86-95: A hypothesis for this study is needed. Furthermore, summarizes the objectives as a single objective.

Response: Hypothesis: We have added a clear hypothesis as suggested. Single Objective: The multiple objectives have been successfully consolidated into one overarching objective, with the original objectives presented as the approaches to achieve it.

Results: The results should be summarized in all their lines (i.e.: delete “In terms of the retention of areas with severe pest infestations,” see line 284).

Response: We agree and have revised the results section to eliminate redundant phrasing and make the statements more direct and objective throughout.

Ls.200-205: This information should be in materials and methods section.

Response: We agree with the reviewer. The described text pertained to the explanation of the Continuous Boyce Index (CBI) evaluation metric, which rightly belongs in the Methods section.

Discussion: The authors fail to contextualize their findings within the framework of existing research on other pest species.

Response: We thank the reviewer for this valuable suggestion. We have now expanded the discussion to relate our findings on the 'asymmetric host-pest response' to broader research on other pest systems

L.395: Delete “In resume,”

Ls.395-402: Given the extent of the analysis presented, the conclusion warrants further development.

Response: We have deleted the informal phrase and have significantly expanded the conclusion to highlight the key findings, their implications, and future research directions, moving beyond a simple summary.

Reviewer 4 Report

Comments and Suggestions for Authors

It is recommended to reduce the similarity index to a maximum of 15%, as it currently has 26%.

The methodology appears appropriate and consistent with the stated objectives. Similarly, as it is a model, it could be improved and include some changes in obtaining data on how these pests are being distributed. However, it is considered that for the scope of this study, the methodological approach is correct.

The article includes 45 references, which are appropriate and related to the topic under discussion. However, the references cited are appropriate and fulfill their purpose of providing context and explanation for the main findings of the paper.

The tables presented are sufficiently clear, and the figures have adequate resolution.

 

Author Response

It is recommended to reduce the similarity index to a maximum of 15%, as it currently has 26%.

The methodology appears appropriate and consistent with the stated objectives. Similarly, as it is a model, it could be improved and include some changes in obtaining data on how these pests are being distributed. However, it is considered that for the scope of this study, the methodological approach is correct.

The article includes 45 references, which are appropriate and related to the topic under discussion. However, the references cited are appropriate and fulfill their purpose of providing context and explanation for the main findings of the paper.

The tables presented are sufficiently clear, and the figures have adequate resolution.

Response: We are sincerely grateful for your positive assessment of our work. In response to the valuable comments from the other reviewers, we have made substantial enhancements to the manuscript, particularly regarding the clarity of data sources, methodological descriptions, and the updating of references, thereby strengthening the reproducibility and overall clarity of our study.

Regarding the similarity index of 26% previously highlighted, we have taken proactive measures to address this issue. Since the specific similarity-checking methodology employed by the journal was not entirely clear to us, we conducted a thorough re-evaluation of the manuscript using Turnitin after temporarily removing the reference list and supplementary materials. The revised document now returns an overall similarity index of 18%. We note that this figure includes matches attributable to the standard MDPI manuscript template, author affiliations, and other standardized, non-substantive text segments. Consequently, we believe the core academic content of the manuscript demonstrates a low level of similarity.

However, should the revised version still not fully meet the journal's specific threshold or policy regarding acceptable similarity levels, we would be immensely grateful if you could provide further guidance. We are fully prepared to undertake additional revisions to ensure the manuscript complies entirely with the journal's publication standards.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for the revision and the response letter. The topic is important and potentially useful for management. However, several fundamental issues remain unresolved. Until the items below are fully addressed, the manuscript does not meet baseline standards for transparency, reproducibility, and methodological rigor. Please do not resubmit this work to this journal in its current form. A major revision is required.

1. Sampling bias and background selection
1.1 The bias correction described in your response letter is not implemented or documented in the manuscript. Add a dedicated Methods subsection that explains how the bias layer was produced, how it was used in MaxEnt, and why this approach is appropriate, with citations to established practices.
1.2 Provide a bias surface map in the supplementary materials and show how background points were weighted or sampled relative to this surface.
1.3 Compare model performance with and without the bias layer and report the impact on evaluation metrics and on mapped areas.
1.4 Discuss reporting heterogeneity in government pest data and show that the bias layer captures this pattern.

2. Occurrence data transparency and quality control
2.1 Provide downloadable CSV files for each species that include coordinates, CRS, dates, source, record identifiers, and coordinate uncertainty.
2.2 Describe your filtering workflow in detail. Report duplicate removal, gross error screening, temporal consistency with baseline climate, and validation of place-name to coordinate conversions.
2.3 Quantify positional uncertainty for text-derived points and explain how uncertain records were handled.
2.4 Include a simple flow diagram of record counts across filtering steps.

3. Model tuning, complexity, and variable selection
3.1 Fifteen predictors for pests with 118 and 109 presences is high. Provide a table listing all candidates, variables removed at each VIF and correlation step, and the ecological rationale for the final set.
3.2 Report permutation importance and jackknife results and explain how they informed variable retention.
3.3 Summarize the ENMeval search space and selection rule and state final parameters per species.
3.4 Provide response curves for key predictors.

4. Spatial validation and evaluation metrics
4.1 Replace random splits with a spatially structured cross-validation scheme and report the results. Describe the blocking strategy and how spatial autocorrelation was mitigated.
4.2 Report mean and variability across replicates for AUC, CBI, omission rates, and other metrics.
4.3 Clarify how the null model was generated and how significance was assessed.

5. Thresholding and binary maps
5.1 Document the derivation of thresholds. Provide ROC curves, TSS, sensitivity, and specificity for each species and specify the software or functions used.
5.2 State whether thresholds were computed per replicate and then averaged or computed on pooled outputs.
5.3 Include a sensitivity analysis showing how alternative reasonable thresholds affect total suitable area and pest overlap.
5.4 Move threshold values into a traceable table while keeping them in figure captions for readability.

6. Future scenarios, transferability, and novelty of conditions
6.1 Justify holding soil and topography constant with appropriate references and discuss likely impacts on transferability.
6.2 Present MESS or MOP maps for all scenarios and explain how negative similarity regions were handled.
6.3 Provide clamping diagnostics and indicate clamped regions on maps.
6.4 Justify the use of a single GCM or discuss uncertainty from GCM choice.

7. Uncertainty communication
7.1 Provide mean and standard deviation maps across replicates in the supplementary materials.
7.2 When reporting areas, include variability across replicates.
7.3 Distinguish uncertainty from model stochasticity, threshold choice, and climate model projections.

8. Figures, tables, and supplementary materials
8.1 Move all supplementary tables and figures, including Table S1, into a properly structured supplementary file and reference them from the main text.
8.2 Use consistent labels for scenarios and time windows and define terms like 2050s and 2090s once and consistently.
8.3 Add scale bars, graticules, boundaries where appropriate, and consistent legends and color ramps across scenarios and species.
8.4 Check units and significant figures in all tables and ensure captions are self-contained.

9. Literature framing and discussion depth
9.1 Revise the Introduction to position the study within recent SDM methodology. Concisely synthesize advances that motivate your design choices, including bias correction, spatial cross-validation, model tuning, threshold selection, and projection to novel conditions.
9.2 Engage with work on host–pest joint assessment to clarify what is conceptually new beyond overlaying independent SDMs.
9.3 Strengthen the Discussion by explaining mechanisms linking patterns to pest biology, host performance, and elevation–climate gradients.
9.4 Add a clear limitations and management implications subsection that translates uncertainty into decision-relevant guidance.

10. Data, code, and computational environment
10.1 Provide a zipped archive or repository link with key scripts for ENMeval runs, MaxEnt fitting, threshold computation, bias layer creation, and mapping. Include session information and software versions.
10.2 List raster sources with version, release year, resolution, resampling method, and CRS. Include metadata and processing steps for any derived rasters.

11. Editorial expectation
These items are mandatory. Please do not resubmit until they are fully implemented and documented. A resubmission that does not provide the requested data, maps, analyses, and citations will not be considered.

Author Response

We thank Reviewer 2 for taking the time to re-evaluate our manuscript and provide detailed comments. We sincerely appreciate your emphasis on methodological rigor and transparency in species distribution modeling.

However, we note that several of the requested items align more closely with methodological research or technical model documentation than with application-oriented studies. Specifically, the requirements for comprehensive code implementation details, extensive sensitivity analyses, and complete uncertainty mapping represent a significant departure from the writing conventions typically employed in applied ecological research using similar modeling approaches, as demonstrated in recent MDPI publications.

If we were to fully address all points, we are concerned that it would shift the focus away from our core scientific question regarding the suitable areas for sea buckthorn under environmental pressures and result in a manuscript length inconsistent with standard article formats. More importantly, such extensive methodological documentation would likely hinder readers' engagement with our primary scientific findings.

Given that these requirements address fundamental questions about the appropriate scope and level of detail for application-focused research articles, we have referred this matter to the editorial office for guidance. We would appreciate the editor's perspective on balancing methodological thoroughness with maintaining the article's focus in this context.

We remain fully committed to addressing any concerns identified as essential by the editorial team, while preserving the clarity and focus appropriate for an application-oriented study.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript “Estimation of the Economic Threshold for the Fall Army Worm Spodoptera frugiperda (Lepidoptera: Noctuidae) in Short stature Maize, Variety Delfín” has been improved, and all my questions were taken into account. I recommend the publication in “Agronomy”.

Author Response

We are very pleased to learn that our manuscript has been accepted for publication. Thank you very much for your consideration.