Predicting Potential Suitable Areas of Dendrocalamus brandisii under Global Climate Change

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

line 21 vs. 141: Different numbers of the analyzed distribution points are indicated.

line 213: "FC=QPH was selected for modeling". Desirable is to explain the type of constructed model in details, i.e. which predictors are used by their quadratic values, which ones by products and which ones are hinges.

lines 222-224 vs. Fig.3: On both diagrams, bio16 variable demonstrates a bigger contribution than Elev. Why was Elev considered to be most important and included in the model? Perhaps, it was due to weaker correlations between Elev and other predictors than between bio16 and them. In fact, the final model is based on the predictors correlating between each other the least (although several significantly correlating ones were initially included in the analysis). It is worth to note this useful final reduction: it demonstrates that the applied inter-predictor correlation threshold of 0.8 is too high; so, to reduce calculations in future, reasonable is to exclude predictors correlating at more than the 0.2 level right away.

lines 232-234: "The existence threshold is usually set to 0.5, which can be regarded as the most suitable growth range for species" This looks reasonable but contradicts the approach described in lines 197-198 ("The distribution probability is divided into unsuitable and suitable areas based on 0.1 and assigned values of 0 and 1"). Did you really use the primitive and illogical division (lines 197-198)? Why? Finally, it is not clear, which an approach is in the base of the maps plotted.

lines 86-87, 411: Mentioned is the weak seed propagation of the investigated bamboo species. At that, interesting is whether it can propagate by culm shanks?

lines 414-415: In Yunnan Province, whether it is necessary to destroy natural forests to obtain more areas for bamboo plantations or not? The produced recommendations should take different consequences into account, not only the direct commercial benefit.

Technical notes:

line 83: "water conservation, soil and water conservation" - simply  "soil and water conservation" can be written once, without duplication

line 341: the term "centroid" should be mentioned in the table title.

Comments on the Quality of English Language

line 62: Here, the word "maintaining" seems to be more suitable than "development"

lines 84-85: again the word "development" is not suitable. The words "using" and "applying" are closer to the sense of these phrases.

line 113: "potential distribution of Cyclobalanopsis gilva in Quercus Rubra". Are Quercus rubra forest(s) implied? At that, species epithet must begin with a lowercase letter 

lines 120-121: More correct seems the following phrase construction: "The geographical distribution range of species and its change trends under different time periods"

Table 4, Fig. 7: The term "retained area" (as in line 201) is better than "reserved area"

Table 4: Perhaps, should be "Change rate", not "Charge"

Author Response

Comments1：[line 21 vs. 141: Different numbers of the analyzed distribution points are indicated.]

Response1:[We noticed the discrepancy in the number of distribution points mentioned in lines 21 and 141. To clarify, the correct number of distribution points analyzed is 142, not the number previously stated in the text. This mistake occurred due to an oversight during data compilation. We apologize for any confusion this may have caused. We have re-verified the data and confirmed that the number of distribution points is indeed 142. This error will be corrected in the revised manuscript.]

Comments2：[line 213: "FC=QPH was selected for modeling". Desirable is to explain the type of constructed model in details, i.e. which predictors are used by their quadratic values, which ones by products and which ones are hinges.]

Response2:[In this model, "selected FC=QPH for modeling" indicates that the variables uvb2 and elev were used in their Quadratic form. The Product terms included are: uvb2bio_18 (the product of uvb2 and bio_18), uvb4bio_18 (the product of uvb4 and bio_18), bio_16bio_18 (the product of bio_16 and bio_18), bio_16elev (the product of bio_16 and elev), bio_18bio_4 (the product of bio_18 and bio_4), and bio_19bio_4 (the product of bio_19 and bio_4). The variables elev, bio_4, bio_18, and bio_19 were used in their Hinge forms.]

Comments3：[lines 222-224 vs. Fig.3: On both diagrams, bio16 variable demonstrates a bigger contribution than Elev. Why was Elev considered to be most important and included in the model? Perhaps, it was due to weaker correlations between Elev and other predictors than between bio16 and them. In fact, the final model is based on the predictors correlating between each other the least (although several significantly correlating ones were initially included in the analysis). It is worth to note this useful final reduction: it demonstrates that the applied inter-predictor correlation threshold of 0.8 is too high; so, to reduce calculations in future, reasonable is to exclude predictors correlating at more than the 0.2 level right away.]

Response3:[The contribution rates of the regularization training gain and test gain of bio16 are higher than those of elev. However, elev was selected for modeling based on the importance of environmental variables to the distribution of Dendrocalamus brandisii for the following reasons:

 

Feature Correlation: From the perspective of the contribution and correlation of environmental factors, it is important to consider the actual needs of the species. bio16 and bio18 features provide similar information. In such cases, the model may choose to retain only one of these features to reduce redundancy and improve the simplicity and stability of the model. elev provides more unique information, making it more advantageous in the model.

Direct Relevance to the Target: Even if bio16 has a high gain contribution in the model, if it has a lower direct correlation with the target variable (species distribution), its role in explanation and prediction might not be as significant as other features. Therefore, the model may prefer features with a higher direct correlation to the target variable.

Model Selection Criteria: When choosing the final model, it is essential to consider not only the performance of the features in training and testing but also their interpretability, uniqueness, and direct correlation with the target variable. If elev has a higher direct correlation with the target variable, its importance in the model might surpass that of bio16.

Relative Importance of Features: In some applications, the importance of features depends not only on their performance in the model but also on their interpretive and decision-making power in practical applications. elev might be more representative than bio16 in explaining climate changes or ecosystem dynamics, thus increasing its importance.

Therefore, even though bio16 performs well in training and testing, its importance in the model may be reduced due to its lower correlation with the target variable and its similarity to bio18 in the information it provides.

We excluded environmental factors with a correlation coefficient greater than 0.8 and a contribution rate to the distribution of Dendrocalamus brandisii below 1%. However, the maximum correlation coefficient among the selected environmental factors did not exceed 0.67. In response to the suggestion to exclude predictive variables with correlations exceeding 0.2, we considered the following points:

Excluding highly correlated variables may result in the loss of valuable information: If these variables uniquely contribute to the target variable in some aspects, their exclusion could negatively impact the model’s overall performance despite their high correlation with other variables.

Correlation between variables may reflect their interactive effects on the target variable: Excluding correlated variables might overlook these potential interactive effects, which could hinder the model's ability to capture complex relationships.

Over-simplifying the model can lead to insufficient data fitting: While simplifying the model can reduce computational burden and enhance interpretability, excessive simplification may render the model too simplistic to capture complex patterns in the data. Excluding variables with correlations above 0.8 helps reduce multicollinearity issues, but over-simplifying might miss intricate relationships that could enhance model performance.

Excluding highly correlated variables might reduce the diversity of the variable set: This could narrow the model’s coverage of the data and affect its adaptability to different environmental conditions.

 

Our method aligns broadly with approaches mentioned in relevant literature: We reviewed pertinent studies, and the details of these references are provided below.

These considerations support our approach to variable selection and emphasize the need to balance simplicity with the complexity required to maintain model performance.：1、Chen, S.; You, C.; Zhang, Z.; Xu, Z. Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios. Forests 2024, 15, 1033. https://doi.org/10.3390/f15061033

2、Song PAN, De-liang PENG, Ying-mei LI, Zhi-jie CHEN, Ying-yan ZHAI, Chen LIU, Bo HONG,Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling,Journal of Integrative Agriculture,Volume 22, Issue 7,2023,Pages 2138-2150,ISSN 2095-3119,https://doi.org/10.1016/j.jia.2023.06.022.]

Comments4：[lines 232-234: "The existence threshold is usually set to 0.5, which can be regarded as the most suitable growth range for species" This looks reasonable but contradicts the approach described in lines 197-198 ("The distribution probability is divided into unsuitable and suitable areas based on 0.1 and assigned values of 0 and 1"). Did you really use the primitive and illogical division (lines 197-198)? Why? Finally, it is not clear, which an approach is in the base of the maps plotted.]

Response4:[Thank you for your thorough review of our work. We fully understand your concerns regarding the survival threshold and the division of distribution probabilities. The methods for classification are logically consistent.

The threshold of 0.1 mentioned in lines 197-198 was used for preliminary suitability classification. This threshold helped us divide the distribution probabilities into unsuitable and suitable areas, assigning values of 0 and 1 respectively, to obtain an initial binary matrix.

However, the survival threshold of 0.5 mentioned in lines 232-234 was used in further analysis to determine the optimal growth range for the species. This threshold correlates with the high habitat suitability of various variables. The threshold for single-factor curves is set at 0.5 based on the high suitability zone of Dendrocalamus brandisii, which can be considered the optimal growth range for the species. This is a good approach to understanding and explaining the model’s behavior and how variables influence the prediction results.

Different threshold selections may lead to different variable sets, thus affecting the model results. The choice of threshold is somewhat subjective and may impact the model's performance and interpretability.

Although these two thresholds differ in value, they each play an important role in our study, and we understand that this may cause some confusion.

 

The maps drawn are based on:

World geographical basemap from the Chinese Academy of Sciences' website (http://www.resdc.cn/data).

Chinese geographical basemap from the National Fundamental Geographic Information System website (http://nfgis.gov.cn), downloaded as a 1:1,000,000 scale vector map of Chinese administrative divisions.]

Comments5：[lines 86-87, 411: Mentioned is the weak seed propagation of the investigated bamboo species. At that, interesting is whether it can propagate by culm shanks?]

Response5:[Yes, Dendrocalamus brandisii can be rapidly propagated through cuttings of the main branches, lateral branches, and secondary branches.]

Comments6：[lines 414-415: In Yunnan Province, whether it is necessary to destroy natural forests to obtain more areas for bamboo plantations or not? The produced recommendations should take different consequences into account, not only the direct commercial benefit.]

Response6:[Thank you for raising this important question. In our research, we place great emphasis on the principles of ecological protection and sustainable development. We understand that it is unacceptable to destroy natural forests for the cultivation of Dendrocalamus brandisii (sweet dragon bamboo). In fact, planting sweet dragon bamboo does not require the destruction of natural forests. We can choose to plant sweet dragon bamboo on already cultivated lands and barren hillsides. Additionally, the extensive root system of sweet dragon bamboo can help in soil and water conservation, thus providing ecological benefits. This way, we can protect natural forests while utilizing the economic value of sweet dragon bamboo. Moreover, our recommendations take into account not only direct commercial interests but also factors such as environmental protection and biodiversity conservation. We believe that only by respecting and protecting nature can our research and work achieve true success.]

Comments7：[line 83: "water conservation, soil and water conservation" - simply  "soil and water conservation" can be written once, without duplication]

Response7:[Already modified.]

Comments8：[line 341: the term "centroid" should be mentioned in the table title.]

Response8:[Already modified.]

Comments9：[line 62: Here, the word "maintaining" seems to be more suitable than "development"]

Response9:[Already modified.]

Comments10：[lines 84-85: again the word "development" is not suitable. The words "using" and "applying" are closer to the sense of these phrases.]

Response10:[Already modified.]

Comments11：[line 113: "potential distribution of Cyclobalanopsis gilva in Quercus Rubra". Are Quercus rubra forest(s) implied? At that, species epithet must begin with a lowercase letter ]

Response11:[Already modified.]

Comments12：[lines 120-121: More correct seems the following phrase construction: "The geographical distribution range of species and its change trends under different time periods" ]

Response12:[Already modified.]

Comments13：[Table 4, Fig. 7: The term "retained area" (as in line 201) is better than "reserved area"]

Response13:[Already modified.]

Comments14：[Table 4: Perhaps, should be "Change rate", not "Charge"]

Response14:[Already modified.]

Reviewer 2 Report

Comments and Suggestions for Authors

While the manuscript titled "Predicting Potential Suitable Areas of Dendrocalamus brandisii under Global Climate Change" is thorough and well-researched, a few areas could benefit from improvement. Firstly, the introduction could provide more detailed background information on the ecological and economic significance of Dendrocalamus brandisii, to better contextualize the study's importance. Additionally, expanding the discussion on the limitations of the MaxEnt model and the potential uncertainties in climate projections would strengthen the study's rigor. Clarifying the selection criteria for the environmental variables used in the model would also enhance the transparency of the methodology. The results section could benefit from more detailed explanations of the key findings, particularly the specific impacts of the identified environmental factors on the species' distribution.

Author Response

Comments1：While the manuscript titled "Predicting Potential Suitable Areas of Dendrocalamus brandisii under Global Climate Change" is thorough and well-researched, a few areas could benefit from improvement. Firstly, the introduction could provide more detailed background information on the ecological and economic significance of Dendrocalamus brandisii, to better contextualize the study's importance. Additionally, expanding the discussion on the limitations of the MaxEnt model and the potential uncertainties in climate projections would strengthen the study's rigor. Clarifying the selection criteria for the environmental variables used in the model would also enhance the transparency of the methodology. The results section could benefit from more detailed explanations of the key findings, particularly the specific impacts of the identified environmental factors on the species' distribution.]

Response1:[The introduction has been revised to include more detailed background information on the ecological and economic significance of Dendrocalamus brandisii, thereby providing a better contextual foundation for the importance of the research, and a relevant applied literature reference has been added.13.Zhu, S. H., Zhao, X. T., Hui, C. M., Zhang, Z. F., Zhang, R. L., Su, W. H., & Liu, W. Y., 2023. Effects of different planting durations of Dendrocalamus brandisii on the soil bacterial community. Journal of Soils and Sediments, 23, 3891–3902. https://doi.org/10.1007/s11368-023-03556-1.；The criteria for selecting environmental variables for the model are described on lines 167-172 of the manuscript; a discussion of the limitations of the MaxEnt model and potential uncertainties in climate predictions has been added on lines 361-374, along with the inclusion of three additional references：44.Pan, S., Peng, D.-l., Li, Y.-m., Chen, Z.-j., Zhai, Y.-y., Liu, C., Hong, B., 2023. Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling. Journal of Integrative Agriculture 22, 2138-2150. https：//doi.org/https://doi.org/10.1016/j.jia.2023.06.022.

45.He, X. G., Liu, H., Zhang, J., Cheng, W., Ding, P., Jia, F. M., Li, Q., & Liu, C., 2023. Predicting potential suitable distribution areas for Juniperus przewalskii in QinghaiProvince of northwestern China based on the optimized MaxEnt model. Journal of Beijing Forestry University, 45(12), 19-31. https://doi.org/10.12171/j.1000-1522.20220515.

Porfirio, L.L., Harris, R.M.B., Lefroy, E.C., Hugh, S., Gould, S.F., Lee, G., Bindoff, N.L., Mackey, B., 2014. Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change. PLOS ONE 9, e113749. https：//doi.org/10.1371/journal.pone.0113749.

The results have been revised to provide a more detailed explanation of the main findings.]

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you, the given explanations are clear. However, some spelling and stylistic errors should be corrected yet (see below).

 

Comments on the Quality of English Language

line 120: must be written "Cyclobalanopsis gilva" (see the title of referred article [25]). At that, your response 11 is not true: again "Quercus Rubra" is written instead "Quercus rubra forests" (species epithet must be written with a lowercase letter, and one tree species, Cyclobalanopsis gilva, cannot inhabit   another tree species, Quercus rubra, but only forests formed by the latter).

Author Response

Comments1：[line 120: must be written "Cyclobalanopsis gilva" (see the title of referred article [25]). At that, your response 11 is not true: again "Quercus Rubra" is written instead "Quercus rubra forests" (species epithet must be written with a lowercase letter, and one tree species, Cyclobalanopsis gilva, cannot inhabit   another tree species, Quercus rubra, but only forests formed by the latter).]

Response1:[Thank you for your careful review of our manuscript. We noticed the error in the sentence you mentioned. This was due to a translation oversight. We have made the necessary corrections, and the revised sentence is as follows:For example, Correlational study found that climatic factors such as the precipitation of the driest month, annual average precipitation, and temperature annual range are key to the potential distribution of Cyclobalanopsis gilva in China.

Thank you again for your correction and patience. We will continue to improve to ensure the accuracy and quality of the manuscript.]