Predicting the Potential Distribution of Endangered Parrotia subaequalis in China

Round 1

Reviewer 1 Report

The study investigates the current and future potential geographical distribution of a critically endangered plant species in China using a presence-only SDM model. I think given the fact that paper deals with an important issue i.e. a critically endangered species it worth considering publication after major changes in methodology.

Although this study is not about the modelling techniques, the authors should explain the method more clearly. How they have dealt with some uncertainty is MaxEnt model set up and data preparation such as sampling bias and variables autocorrelation. What about the suitability of correlative models for such study? Why correlative model rather that a mechanistic model as some argue that the correlation between the species and environment may cease to exist/change in future decades. All these needs to be at least discussed in introduction and discussion. Please refer to the “pdf” file for more detailed comments

My main concern in the way that results are presented. The author refer to He et al. In categorizing the prediction. I am very surprised such an important point has not been pick up in that publication. This is a very risky and dangerous practice unless proper justifications are provided which may differ for each species. Depending on the purpose of the study, the threshold (at least the lower threshold) for this purpose can be chosen from the Table produced by MaxEnt where “Cumulative threshold”, Logistic threshold, and omission rates are provided. If one is after equal sensitivity and specificity then such threshold is selected. If one is more focused on conservation practices, the specificity is preferred etc. I suggest authors have a look at papers discussing this issue such as the following papers:

• Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of biogeography, 41(4), 629-643.
• Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866.(Section 4.2)
• NaroueiKhandan et al.  (2020). Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus. Plant Pathology, 69(1), 17-27.
• (model consensus section)
• Liu, Canran, Matt White, and Graeme Newell. "Selecting thresholds for the prediction of species occurrence with presence‐only data." Journal of biogeography 40.4 (2013): 778-789.
• Khandan, H.A. Ensemble models to assess the risk of exotic plant pathogens in a changing climate. Diss. Lincoln University, 2014.

I couldn’t find supplementary files (not sure if journal asks for it or it’s there and I didn’t see it!). The presence files which include the coordinate should be included in a table with the sources that data were acquitted so other can use them if needed to replicate the study. The discussion part must be improved ( comment in pdf file(. 

Comments for author File: Comments.pdf

Author Response

Dear Editor,

 

We would like to thank you, and the two referees for their constructive comments on previous manuscript (Forests-1853214), which is entitled “Predicting the potential distribution of endangered Parrotia subaequalis in China”. We have read and studied their comments and suggestions carefully and have made corrections which we hope meet with their approval. Please see three attachments, including a revision explanation, a revised manuscript (Word edition) and an accepted manuscript (Clean edition). The following are the correspondences to you and the reviewers concerning the comments and suggestions about the manuscript.

We submitted the original manuscript on 22nd of July, 2022. The decision from the Forests’ editorial office is “Pending major revisions”. Due to delay replying, the editor kindly suggests us to resubmit the revised manuscript.

Therefore, we have now finished resubmitting the revised manuscript. Here attached is the responding letter, revised manuscript and appendix concerning the occurrence records of Parrotia subaequalis in China.

 

 

To Reviewer 1

Part A: Comments from Reviewer 1

1. “The study investigates the current and future potential geographical distribution of a critically endangered plant species in China using a presence-only SDM model. I think given the fact that paper deals with an important issue i.e. a critically endangered species it worth considering publication after major changes in methodology.”

√ We express gratitude to the first reviewer for his/her critical review.

 

2. “Although this study is not about the modelling techniques, the authors should explain the method more clearly. How they have dealt with some uncertainty is MaxEnt model set up and data preparation such as sampling bias and variables autocorrelation. What about the suitability of correlative models for such study? Why correlative model rather that a mechanistic model as some argue that the correlation between the species and environment may cease to exist/change in future decades. All these needs to be at least discussed in introduction and discussion. Please refer to the “pdf” file for more detailed comments.”

√ Thank you very much for your comment.

The first question is about the uncertainty of MaxEnt model in the manuscript. Firstly, we collected 211 occurrence records of Parrotia subaequalis in China. Then, we deleted incorrect or duplicate record points. Thirdly, we used the Spatially Rarefy Occurrence Data for SDMs tool (i.e. SDMtoolbox 2.0) to retain only one distribution point in each 1km × 1km grid. Thus we obtained 115 occurrence records of this species.  In doing so, this refinement of presence data will match resolution of the climate data to avoid environmental bias due to possible spatially auto-correlated presence points. Meanwhile, we also used the Spatial Analyst Tools in ArcGIS 10.6 to extract the values of 19 bioclimatic variables at these 115 distribution points. The Pearson correlation coefficient (r) between bioclimatic variables was tested by R 4.1.3 to eliminate the variables with the lower percent contribution among those | r | > 0.8.

The second question is about “the suitability of correlative models for such study”. MaxEnt model, one of the correlative models, has been widely and successfully applied to predict the distribution of rare and endangered plant species in recent years. Such examples are Trochodendron aralioides (Chiu et al., 2022), Glyptostrobus pensilis (Ye et al., 2022), and Ostrya rehderiana (Tang et al., 2022).

The third question is the reason that we selected the correlative model (i.e. MaxEnt) rather than a mechanistic model in the text. Our research material is P. subaequalis, which is an ancient tree species. It is endemic to China, and is a small deciduous tree in the family Hamamelidaceae. As a Tertiary relict plant, this species usually has a quite conservative correlation with its environment. More recently, a new study finds that the interaction between the species of Parrotia and their herbivores persisted over at least 15 million years spanning eastern Asia to western Europe (Adroit et al., 2020).

In the current study, our material is an endangered tree species endemic to China, which is totally different from pathogen (i.e. Valsa mali) or an invasive herb (i.e. Alternanthera philoxeroides) (Xu et al., 2020). For those species such as pathogen or invasive plant species, the correlation between those species and their environments may change a lot or even not exist under global change. In contrast, for Parrotia subaequalis, we do not think that such relationship may have a great change in future several decades (i.e.2050s, 2070s).

In brief, we have made some revisions in the section of Discussion according to the suggestions of the Reviewer 1. Please see Line 300-312.

 

References

Adroit, B.; Zhuang, X.; Wappler, T.; Terral, J.-F.; Wang, B. A case of long-term herbivory: specialized feeding trace on Parrotia (Hamamelidaceae) plant species. R. Soc. Open Sci. 2020, 7, 201449. http://dx.doi.org/10.1098/rsos.201449

Chiu, C.-A.; Matsui, T.; Tanaka, N.; Lin, C.-T. Exploring the potential distribution of relic Trochodendron aralioides: An approach using open-access resources and free software. Forests 2021, 12, 1749. https://doi.org/10.3390/f12121749

Tang, S.L.; Song, Y.B.; Zeng, B.; Dong, M. Potential distribution of the extremely endangered species Ostrya rehderiana (Betulaceae) in China under future climate change. Environ. Sci. Pollut. Res. 2022, 29, 7782–7792. https://doi.org/10.1007/s11356-021-16268-1

Xu, W.; Sun, H.; Jin, J.; Cheng, J. Predicting the potential distribution of apple canker pathogen (Valsa mali) in China under climate change. Forests 2020, 11, 1126. https://doi.org/10.3390/f11111126

Ye, X.; Zhang, M.; Yang, Q.; Ye, L.; Liu, Y.; Zhang, G.; Chen, S.; Lai, W.; Wen, G.; Zheng, S.; et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis. Forests 2022, 13, 257. https://doi.org/10.3390/f13020257

 

3. “My main concern in the way that results are presented. The author refer to He et al. In categorizing the prediction. I am very surprised such an important point has not been pick up in that publication. This is a very risky and dangerous practice unless proper justifications are provided which may differ for each species. Depending on the purpose of the study, the threshold (at least the lower threshold) for this purpose can be chosen from the Table produced by MaxEnt where “Cumulative threshold”, Logistic threshold, and omission rates are provided. If one is after equal sensitivity and specificity then such threshold is selected. If one is more focused on conservation practices, the specificity is preferred etc. I suggest authors have a look at papers discussing this issue such as the following papers:

Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of biogeography, 41(4), 629-643.

Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866.(Section 4.2)

NaroueiKhandan et al. (2020). Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus. Plant Pathology, 69(1), 17-27.

(model consensus section)

Liu, Canran, Matt White, and Graeme Newell. "Selecting thresholds for the prediction of species occurrence with presence‐only data." Journal of biogeography 40.4 (2013): 778-789.

Khandan, H.A. Ensemble models to assess the risk of exotic plant pathogens in a changing climate. Diss. Lincoln University, 2014.”

√ Thank you very much for your critical comments.

First of all, I am afraid that for the Reviewer 1, there may be a misunderstanding about the citation which is mentioned in his/her comments. That is to say, “The author refer to He et al.” In fact, we did not cite this article in the original Manuscript! Furthermore, we do not know what this article (i.e. He et al.) refers to.

Currently, there is no consensus concerning the threshold of predicted suitable habitats. In the original Manuscript, we equally divided the distribution map into four categories (i.e. 0.00-0.25; 0.25-0.50; 0.50-0.75; 0.75-1.00). Such a practice is also seen in other related studies (Gülçin et al., 2021; Jiang et al., 2022). However, based on the reviewer’s advice we have carefully read the listed references and other related papers. Then we realize that it seems more appropriate to choose Max SSS (i.e. maximizing the sum of sensitivity and specificity) as the threshold of presence and absence, compared with the previous method. For rare and endangered plants, the Max SSS approach can produce higher sensitivity in most cases, and recently this practice has been used in other related studies (Xu et al., 2021; Sreekumar et al., 2022). According to the result of the MaxEnt modeling, the maximum value of sum of sensitivity plus specificity is equal to 0.112. Therefore, in the revised Manuscript we set the range from 0 to 0.1 as unsuitable habitat, and we equally divide the others into three categories including low suitable habitat (0.1–0.4), moderately suitable habitat (0.4–0.7), and highly suitable habitat (0.7–1.0).

Accordingly, we have redrawn the Figure 4 and Figure 5, and redone the Table 3. Please see more details on Line 182-189.

 

References

Gülçin, D.; Arslan, E.S.; Orücü, O.K. Effects of climate change on the ecological niche of common hornbeam (Carpinus betulus L.). Ecol. Inform. 2021, 66, 101478. https://doi.org/10.1016/j.ecoinf.2021.101478

Jiang, R.; Zou, M.; Qin, Y.; Tan, G.; Huang, S.; Quan, H.; Zhou, J.; Liao, H. Modeling of the potential geographical distribution of three Fritillaria species under climate change. Front. Plant Sci. 2022. 12, 749838. https://doi.org/10.3389/fpls.2021.749838

Liu, C.; Newell, G.; White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 2016, 6, 337-348. https://doi.org/10.1002/ece3.1878

Sreekumar, E.R.; Nameer, P.O. A MaxEnt modelling approach to understand the climate change effects on the distributional range of White-bellied Sholakili Sholicola albiventris (Blanford, 1868) in the Western Ghats, India. Ecol. Inform. 2022, 70, 101702. https://doi.org/10.1016/j.ecoinf.2022.101702

Xu, Y.; Huang, Y.; Zhao, H.; Yang, M.; Zhuang, Y.; Ye, X. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 2021, 12, 429. https://doi.org/10.3390/f12040429

 

4. “I couldn’t find supplementary files (not sure if journal asks for it or it’s there and I didn’t see it!). The presence files which include the coordinate should be included in a table with the sources that data were acquitted so other can use them if needed to replicate the study. The discussion part must be improved ( comment in pdf file(.”

√ Thank you very much for your comments. We have uploaded the coordinates of occurrence records of Parrotia subaequalis in China as an appendix. Moreover, we have also made some improvements in the section of Discussion according to the suggestions of Reviewer 1.

 

 

 

Part B: Notes from Reviewer 1

1. “Ln. 58-61: Re-write this, something like:

Although the current distribution of the ... seems to be limited and narrow, discovery of new localities of P. subaequalis in Shangcheng ...... indicates that actual spatial distribution of the species be wider than its known distribution. ”

√ Thank you very much for your comment. We have accepted your suggestion and made a revision.

Please see Line 63-69 in the revised Manuscript.

 

2. “Ln. 75: Delete “of all”, “accurately”.”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 53, 84 in the revised Manuscript.

 

3. “Ln. 101: Delete “-al”. ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 111 in the revised Manuscript.

 

4. “Ln. 104:Delete “in”. ”

√ Thank you for your comment.

We have accepted the suggestion, and made a revision herein.

Please see Line 114 in the revised Manuscript.

 

5. “Ln. 111: It is confusing the way its written. Due to resolution of the climate data, one point per pixel (~1 sq Km) we retained. ?”

√ Thank you very much for your critical review. We have accepted the suggestion, and rewritten these sentences in the section of Method.

Please see Line 118-123in the revised Manuscript.

 

6. “Ln. 112: Change “Thus” to “In total”. ”

√ Thanks for your comment. We have made a revision herein.

Please see Line 123 in the revised Manuscript.

 

7. “ 113: Change “employed” to “used”. ”

√ Thank you very much for your comment. We have changed the word “employed” to “used” herein.

Please see Line 123 in the revised Manuscript.

 

8. “Ln. 114-116: Does figure 2 represent all these locations?”

√ Thank you for your comment. Yes, Figure 2 represents all these locations.

 

9. “ 116-118: Delete “These points included almost all known locations of P. subaequalis. The data files were saved as .csv format for the subsequent construction of MaxEnt model.” ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 126 in the revised Manuscript.

 

10. “Ln. 126: I wonder why the new version of the data was not used which covers 1970-2000? ”

√ Thank you very much for your comment.

Although WorldClim 2.1 version was released in 2017 by Fick and Hijmans (2017), WorldClim 1.4 version has been widely used for species potential distribution prediction. Furthermore, it seems much convenient to compare our result of the current study with others (Ren et al., 2020; Cerasoli et al., 2022; Chen et al., 2022 ) because both use the same edition of climate data. Therefore, we chose WorldClim 1.4 version of the 1960-1990 data.

Additionally, in the original manuscript we failed to explicitly state the version of climate data. We have made revisions herein.

Please see Line 130-141 in the revised Manuscript.

 

References

Cerasoli, F.; D’Alessandro, P.; Biondi, M. Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe. Ecol. Evol. 2022, 12, e8430. https://doi.org/10.1002/ece3.8430

Chen, Y.; Li, Y.; Mao, L. Combining the effects of global warming, land use change and dispersal limitations to predict the future distributions of east Asian Cerris oaks (Quercus Section Cerris, Fagaceae) in China. Forests 2022, 13, 367. https://doi.org/10.3390/f13030367

Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302-4315. https://doi.org/10.1002/joc.5086

Ren, Z.C.; Zagortchev, L.; Ma, J.X.; Yan, M.; Li, J.M. Predicting the potential distribution of the parasitic Cuscuta chinensis under global warming. BMC Ecol. 2020, 20, 28. https://doi.org/10.1186/s12898-020-00295-6

 

11. “ 133-134: Move this table (Table 1) to Results section.”

√ Thank you very much for your comment.

We have accepted the suggestion, and moved the original Table 1 from the section of Methods to Results.

Please see Line 217 in the revised Manuscript.

 

12. “ 144-145: Change the sentence to “and variables that contributed the most to the model gain were selected.””

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 152 in the revised Manuscript.

 

13. “ 152: Change the sentence to “selected climate variables were ...” ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 159 in the revised Manuscript.

 

14. “ 165: Change the sentence to “were used to assess the model performance. ” ”

√ We appreciate this valuable comment, and have accepted your suggestion. 

Please see Line 172-173 in the revised Manuscript.

 

15. “ 176-177: I have noticed this kind of categorization but it should be noticed that such categorization can be mis-leading and dangerous. The values and categories should be interpreted based on model performance criteria.

It might be acceptable to do such categorization to monitor changes under different climate scenarios but the issue should be discussed in Discussion section. Have a look at these two :

Narouei-Khandan, H. A. (2014). Ensemble models to assess the risk of exotic plant pathogens in a changing climate (Doctoral dissertation, Lincoln University).

https://doi.org/10.1111/ppa.13111 ” ”

√ Thank you very much for your comment. This is the same question as in Comment 3.

We have accepted your suggestion, and added a sentence in “4.4. Conservation implications for P. subaequalis” in the section of Discussion. That is “We set the cut off threshold for species presence and absence through maximizing the sum of test sensitivity and specificity, and thus such an approach can ensure the reliability of suitability classification of P. subaequalis.”

Please see more details in “Part A: Comments from Reviewer 2”.

Please see Line 182-189, 438-441 in the revised Manuscript.

 

16. “ 188: Delete “In the meanwhile”. ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 198 in the revised Manuscript.

 

17. “ 190: Change the sentence to “that model prediction had high.... ”. ”

√ Thank you very much for your comment.

We have accepted your suggestion, and made a revision.

Please see Line 200 in the revised Manuscript.

 

18. “ 199: what do you mean by “normalized”?”

√ Thank you very much for your comment. We have accepted your suggestion, and made a revision.

Please see Line 209-210 in the revised Manuscript.

 

19. “ 204-206: No need to mention this really.”

√ Thank you very much for your comment. We have accepted your suggestion, and made a revision.

Please see Line 214-216 in the revised Manuscript.

 

20. “ 208: Change “ existence” to “Presence”.”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 221 in the revised Manuscript.

 

21. “ 213: Mention what they are instead on codes such as precipitation in the driest quarter between .....”

√ Thank you very much for your comment. We have added the full names of these bioclimatic variables herein.

Please see Line 235-238 in the revised Manuscript.

 

22. “ 270: This section misses important elements. Please refer to my comments. In short, the biological relevance of the variables which had the highest gain in model such as Bio01, Bio 09 and Bio17 should be discussed in more details. There should a small paragraph about the limitations of the SDMs in general or about MaxEnt itself. Please refer to the suggested publications for this purpose.

Narouei-Khandan, H. A. (2014). Ensemble models to assess the risk of exotic plant pathogens in a changing climate (Doctoral dissertation, Lincoln University). Chapter 9

And https://doi.org/10.1111/ppa.13111”

√ Thank you very much for your comments. We have accepted the suggestions, and made some revisions in the section of Discussion.

Please see Line 300-318, 324-342 in the revised Manuscript.

 

23. “ 277: The model performed well. We can say it did well based on training data buy accuracy is has different implications. ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 322 in the revised Manuscript.

 

24. “ 277-278: Change the sentence to “therefore, the model explains well the potential distribution of P. subaequalis based on current distribution. ” ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 323-324 in the revised Manuscript.

 

25. “ 282-285: Re-write and connect it more to sound more biologically meaningful. ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.

Please see Line 327-330 in the revised Manuscript.

 

26. “ 288: Do you mean water needs? ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 333 in the revised Manuscript.

 

27. “ 289: Change the sentence to “ experiment, Yue et al. showed that ....”. ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 334 in the revised Manuscript.

 

28. “ 296: Change the sentence to “ merely illustrated .....”. ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 347 in the revised Manuscript.

 

29. “ 303-305: I am not sure what authors means here, this issue would be a factor for all prediction models and more to consider in discussion or when one set up monitoring or response programs. ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.

Please see Line 354-358 in the revised Manuscript.

 

30. “ 308: Change “a new population locality” to “a suitable habitat”. ”

√ Thank you very much for your correction. We have accepted the suggestion.

Please see Line 366 in the revised Manuscript.

 

31. “ 317: Change “seriously” to “extremely” .”

√ Thank you very much for your correction. We have accepted the suggestion.

Please see Line 377 in the revised Manuscript.

 

32. “ 321,324: Delete “At the same time”, “ First of all” .”

√ Thank you very much for your comments. We have accepted your suggestions.

Please see Line 381, 384 in the revised Manuscript.

 

33. “ 330: Change the sentence to “the species more likely ...” ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a modification herein.

Please see Line 391 in the revised Manuscript.

 

34. “ 335-337: Not really informative and not much adding values, this could be true for many species. ”

√ Thank you very much for your comment. We have deleted this paragraph, and made a revision in the first paragraph of “4.2. Predicted habitat suitability for P. subaequalis under current scenario”.

Please see Line 354-358 in the revised Manuscript.

 

35. “ 343, 348: Delete “Firstly” and “jointly” ”

√ Thank you very much for your comments. We have accepted the suggestion.

Please see Line 402 and 408 in the revised Manuscript.

 

36. “ 345: Change “Secondly” to “In addition”. ”

√ Thank you very much for your comment. We have accepted your suggestion, and made a revision.

Please see Line 405 in the revised Manuscript.

 

37. “ 349: I suggest avoid using abbreviations such as CC DB etc... in discussion so the reader can better follow the text. ”

√ We appreciate this valuable comment, and have changed the abbreviated names to full ones in the section of Discussion.

 

38. “ 349: Use scientific words such as insignificant or slightly, or the change was negligible ( if thats the case). ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 411 in the revised Manuscript.

 

39. “ 361-362: Change the sentence to “It is plausible for ...” ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.

Please see Line 426 in the revised Manuscript.

 

40. “ 373: Delete “by”. ”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 439 in the revised Manuscript.

 

41. “ 410: So many other species do the same. Not a informative... ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.

Please see Line 484-485 in the revised Manuscript.

 

 

To Reviewer 2

Part A: Comments from Reviewer 2

“This paper used a species distribution modeling approach to model and predict the current and future potential distribution of an endangered species named Parrotia subaequalis in China. The authors then introduced the main factors influencing the species distribution and gave the response curve of this species to the three climate variables. The methodology used in this study is solid. However, my main concern is the fact that they obtained their “current climate data” from the WorldClim website and used it as a basis for modeling the current distribution of the specie in China. These data are the average of 1960-1990 as stated in the method section. The data for this period time cannot be regarded as the present climate condition as climate conditions changed considerably in these two recent decades.”

√ We appreciate this critical comment.

Regarding the “current climate data” mentioned by the Reviewer 2, our understanding is as follows. At present, the climate data downloaded from the Worldclim website have two versions including Worldclim 1.4 and 2.1, both of which can be used in MaxEnt model. WorldClim 1.4 version was released in 2005 (Hijmans et al., 2005), and WorldClim 2.1 version in 2017 (Fick and Hijmans, 2017).

In our opinion, the “current climate data” in each version is not equivalent to that of actual climate condition (i.e. “these two recent decades”). In Worldclim version 1.4, the current climate data use the average for the years 1960-1990, while in Worldclim version 2.1, the current climate data use the average for the years 1970-2000. Just as Reviewer 2 states, it is likely that the actual climatic condition has considerably changed over the past two decades. In fact, the “current climate data” in each of the two versions cannot be equivalent to the real climate data in the past two decades. Generally, the “current climate data” from the Worldclim website is only an approximation of the actual climate situation.

In this study we made prediction using MaxEnt model based on 115 distribution points of P. subaequalis and their climate data from Worldclim version 1.4 [i.e. current data from 1960 to 1990, future data in 2050s (2041-2060) and 2070s (2061-2080)]. WorldClim 1.4 version has been widely used for species potential distribution prediction. Furthermore, it seems much convenient to compare our result of the current study with others (Ren et al., 2020; Cerasoli et al., 2022) because both use the same edition of climate data.

According to the prediction results, we have delineated the possible distribution of P. subaequalis under current climate scenario, and analyzed the distribution shift in suitable areas under future climate scenarios. We think that the reliability of MaxEnt modeling depends mainly on the representation of occurrence record and climate data as well as optimization of modeling algorithm.

Firstly, the sampling data in this study came from our extensive field surveys for P. subaequalis wild populations, published literatures and related websites. We deleted incorrect or duplicate record in these obtained 211 points. Then we used the Spatially Rarefy Occurrence Data for SDMs tool (i.e. SDMtoolbox 2.0) to avoid spatially auto-correlated between presence points. Thus we got a total of 115 occurrence records of this species. The predicted distribution is consistent with the known occurrence records of P. subaequalis, which verified the sampling data’ representative.

Secondly, nowadays most scholars have distinctive views on choosing climate data from different global climate models (GCMs). For this reason, we selected the climate model data with three sets of GCMs which are most widely used (Chen et al., 2022) to increase the reliability of climate data. Accordingly, we calculated the equally-weighted mean values of Beijing Climate Center Climate System Model Version 1.1 (BCC-CSM1-1), the Community Climate System Model version 4 (CCSM4) and An Earth System Model based on the Model for Interdisciplinary Research on Climate (MIROC-ESM) to obtain a suite of future climate data.

Thirdly, we used the ENMeval package in R 4.1.3 to select the optimal model tuning parameters rather than the default parameters. Furthermore, the AUC and TSS values in this study were both greater than 0.9, indicating that the model prediction had high credibility and accuracy.

 

References

Cerasoli, F.; D’Alessandro, P.; Biondi, M. Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe. Ecol. Evol. 2022, 12, e8430. https://doi.org/10.1002/ece3.8430

Chen, Y.; Li, Y.; Mao, L. Combining the Effects of Global Warming, Land Use Change and Dispersal Limitations to Predict the Future Distributions of East Asian Cerris Oaks (Quercus Section Cerris, Fagaceae) in China. Forests 2022, 13, 367. https://doi.org/10.3390/f13030367

Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302-4315. https://doi.org/10.1002/joc.5086

Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005, 25, 1965-1978. https://doi.org/10.1002/joc.1276

Ren, Z.C.; Zagortchev, L.; Ma, J.X.; Yan, M.; Li, J.M. Predicting the potential distribution of the parasitic Cuscuta chinensis under global warming. BMC Ecol. 2020, 20, 28. https://doi.org/10.1186/s12898-020-00295-6

 

Part B: Notes from Reviewer 2

Abstract

1.“Ln. 17: You should indicate what does it mean DB, TM, and CC. ”

√ We appreciate this valuable comment, and have accepted the suggestion.

Please see Line 17-18 in the revised Manuscript.

 

2.“Ln. 19: Did you measured fragmentation patterns or indices?”

√ Thanks for your comment. We did not measure fragmentation patterns or indices.

Even so, our results indicated that the fragmentation degree of the Tianmu Mountain Area (TM) suitable habitat increased in the future relative to current scenario, which is shown in Figure 4 and 5. Similar to the situation in Tianmu Mountain Area (TM), the degree of habitat fragmentation in Dabie Mountain Area (DB) also showed an increasing trend under the future climate scenarios relative to current scenario.

 

Introduction

3.“Ln. 51: Can you mention its photosynthesis pathway? ”

√ Thanks for your comment. As far as we know, no reference states the photosynthetic pathway of P. subaequalis. We speculate that this species probably belongs to C3 pathway. However, according to the suggestion of the reviewer, we add a reference concerning its photosynthetic capacity, and made a revision herein.

 

Methods

4.“Ln. 108: What was your criteria to consider a presence point for the species?”

√ Thanks for your comments.

Most researchers usually obtain the occurrence point data of an endangered plant species by collecting its occurrence records from published literatures and related websites, together with some field work when they predict its potential distribution under current and future scenarios (Dhyani et al., 2021; Ye et al., 2022). In this study, we followed the methodology. That is to say, if a sample or specimen occurs in a certain site according to literatures or investigations, such a site will be initially taken as an occurrence point for this species.

Firstly, we collected occurrence points of P. subaequalis from our extensive field surveys (Li and Zhang, 2015; Zhang et al., 2016; Liu et al., 2021) and related references. After deleting incorrect or duplicate record points, we then used the Spatially Rarefy Occurrence Data for SDMs tool (i.e. SDMtoolbox 2.0) to retain only one distribution point in each 1km × 1km grid. This approach of rarefying record points is based on the resolution of bioclimatic data to reduce the spatially auto-correlated occurrence points (Radosavljevic et al., 2014; Narouei-Khandan et al., 2020). Finally, we obtained 115 occurrence points of P. subaequalis in this study.

For more details, please see “2.1 Species occurrence data” in the section of Methods.

 

References

Dhyani, A.; Kadaverugu, R.; Nautiyal, B.P.; Nautiyal, M.C. Predicting the potential distribution of a critically endangered medicinal plant Lilium polyphyllum in Indian Western Himalayan Region. Reg. Environ. Change 2021, 21, 30, https://doi.org/10.1007/s10113-021-01763-5

Li, W.; Zhang, G.F. Population structure and spatial pattern of the endemic and endangered subtropical tree Parrotia subaequalis (Hamamelidaceae). Flora 2015, 212, 10-18, https://doi.org/10.1016/j.flora.2015.02.002

Liu, J.; Zhang, G.F.; Li, X. Structural diversity and conservation implications of Parrotia subaequalis (Hamamelidaceae), a rare and endangered tree species in China. Nat. Conserv. 2021, 44, 99–115, https://doi.org/10.3897/natureconservation.44.69404

Narouei-Khandan, H.A.; Worner, S.P.; Viljanen, S.L.H.; van Bruggen, A.H.C.; Jones, E.E. Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus. Plant Pathol. 2020, 69, 17–27, https://doi.org/10.1111/ppa.13111

Radosavljevic, A.; Anderson, R.P. Making better MAXENT models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 2014, 41, 629–643.  https://doi.org/10.1111/jbi.12227

Ye, X.; Zhang, M.; Yang, Q.; Ye, L.; Liu, Y.; Zhang, G.; Chen, S.; Lai, W.; Wen, G.; Zheng, S.; et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis. Forests 2022, 13, 257. https://doi.org/10.3390/f13020257

Zhang, G.F.; Yao, R.; Jiang, Y.Q.; Chen, F.C.; Zhang, W.Y. Intraspecific and interspecific competition intensity of Parrotia subaequalis in different habitats from Wanfoshan Nature Reserve, Anhui Province. Chinese J. Ecol. 2016, 35, 1744–1750, https://doi.org/10.13292/j.1000-4890.201607.029

 

5.“Ln. 125: The data for this period time cannot be regarded as the present climate conditions as climate condition changed considerablt in these two recent decades. ”

√ Thank you very much for your comment.

We have answered that question, please see “Part A: Comments from Reviewer 2” for details.

 

6.“Ln. 162: first state the training test and then test set. ”

√ Thanks for your comment. We have accepted the suggestion.

Please see Line 168-169 in the revised Manuscript.

 

Results

7. “Ln. 211: Describe the shape of the response curves. ”

√ Thank you very much for your comment. We have accepted the suggestion, and added the following sentences in the text:

“When precipitation of driest quarter (Bio17) exceeded 117.6 mm, P. subaequalis was in a suitable survival condition (existence probability > 0.5). With the increase of Bio17, the existence probability gradually increased and reached a peak (0.67) at 128.9 mm (Figure 3). The probability of existence then decreased in the range of 128.9-146.4 mm. Mean temperature of driest quarter (Bio9) ranged from 2.3℃ to 5.8℃, which was suitable for its growth, and the survival probability first increased and then decreased in this case, reaching the maximum at 3.1℃ (0.59). Similarly, when annual mean temperature (Bio1) was above 12.6℃, the survival probability exceeded 0.5. With the increase of Bio1, its existence probability for P. subaequalis increased to a maximum as high as 0.65 at 13.8℃.”

Please see Line 225-234 in the revised Manuscript.

 

8. “Ln. 213: there is no need to report more than one decimal values. ”

√ Thanks for your comment. We have accepted the suggestion.

Please see Line 225-234 in the revised Manuscript.

 

9. “Ln. 225: Remove "o".”

√ Thanks for your comment. I am afraid that I can not agree with you. The proportion herein refers to the proportion of the current suitable area of P. subaequalis in China's total territory. Because its value is so tiny, it is expressed as a permillage (i.e. ‰), rather than a percentage (i.e. %) in the text.

 

10. “Ln. 243: Have you measured fragmentation status in this area? ”

√ Thanks for your comment. This is the same question as in Note 2.

We did not measure the fragmentation status by a certain index in this area. However, we have drawn the maps of suitable habitats in this area under current and future climate scenarios. Moreover, our results indicated that the fragmentation degree of the Tianmu Mountain Area (TM) suitable habitat increased in the future relative to current scenario, which is shown in Figure 4 and 5.

According to Reviewer 2’s comment, we have made a revision herein.

Please see Line 267-268 in the revised Manuscript.

 

11.“Ln. 262: Have you discussed this? reason?”

√ We appreciate this valuable comment.

To date, most of endangered plants appear inconsistent trend in response to future climate change when predicting their potential habitats by MaxEnt model. To the best of our knowledge, their habitat changes can be roughly divided into three categories: (1) the increasing type, such as Thuja sutchuenensis (Qin et al., 2021); (2) the decreasing type, such as Glyptostrobus pensilis (Ye et al., 2022); (3) the fluctuating type, such as Fritillaria cirrhosa (Jiang et al., 2022).

One of the objectives in this study is “determining the responses of P. subaequalis under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) in the future (2050s and 2070s)”, rather than revealing the underlying mechanism of how it changes in different scenarios.

According to Reviewer 2’s suggestion, we have provided a possible explanation in the text. That is: “This is consistent with the change trend of suitable habitat of three Fritillaria species (Jiang et al., 2022), which appear a trend of rising first and then declining in suitable habitats. Our results indicated that P. subaequalis may be differently adapted to the range of concentration pathways.”

Please see Line 287-290 in the revised Manuscript.

 

References

Jiang, R.; Zou, M.; Qin, Y.; Tan, G.; Huang, S.; Quan, H.; Zhou, J.; Liao, H. Modeling of the potential geographical distribution of three Fritillaria species under climate change. Front. Plant Sci. 2022. 12, 749838. https://doi.org/10.3389/fpls.2021.749838

Ye, X.; Zhang, M.; Yang, Q.; Ye, L.; Liu, Y.; Zhang, G.; Chen, S.; Lai, W.; Wen, G.; Zheng, S.; et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis. Forests 2022, 13, 257. https://doi.org/10.3390/f1302025

Qin, A.; Liu, B.; Guo, Q.; Bussmann, R.W.; Ma, F.; Jian, Z.; Xu, G.; Pei, S. Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch, an extremely endangered conifer from southwestern China. Glob. Ecol. Conserv. 2017, 10, 139–46. https://doi.org/10.1016/j.gecco.2017.02.004

 

Conclusion

12.“Ln. 412: Have you mentioned its reason? ”

√ Thanks for your valuable comment.

According to Reviewer 2’s suggestion, we have made a revision and added a reference. For more details, please see “4.2 Predicted habitat suitability for P. subaequalis under current scenario” in the section of Discussion.

Please see Line 359-362 in the revised Manuscript.

 

Reference

Hao, R.M.; Wei, H.T. A new combination of Hamamelidaceae. Acta Phytotaxon. Sin. 1998, 36, 80.

 

Tables

13. “Ln. 229: Table 3, Lowly" is not an appropriate word. ”

√ Thanks for your correction. We have accepted the suggestion.

Please see Line 253 (Table 3) in the revised Manuscript.

 

14. “Ln. 229: Table 3, Use smaller font to adjust the words and lines in this table. ”

√ Thanks for your comment. We have accepted your suggestion, and made a modification.

Please see Line 253 (Table 3) in the revised Manuscript.

 

15. “Ln. 229: Table 3, How it is possible that the size of suitable area for this species increase up to 2050 and then it decrease in 2070? I do not understand. ”

√ Thank you very much for your comment.

This is the same question as in Note 11. Please see our reply to Note 11.

 

Figures

16. “Ln. 217: Figure 3, Write the full name of these variable in the first axes in these figures.”

√ Thank you very much for your critical review. We have accepted the suggestion.

Please see Line 242 in the revised Manuscript.

 

 

 

To Editor (Alina Li)

√ We have accepted the suggestions and made revisions accordingly in the revised Manuscript.

 

Besides, we have made further modifications after carefully checking the whole manuscript. They are as follows.

√ We have recalculated the data in the manuscript and redrawn Figure 4 & 5.

√ We have added thirteen references and deleted two references.

√ We have checked the manuscript thoroughly in order to make sure the formats meet the requirements of Forests.

 

On behalf of all authors, I hope that the revised manuscript will be acceptable for publication in Forests.

If you have any question about this paper, please do not hesitate to contact me.

 

Best regards,

Guangfu Zhang

2022/9/20

Author Response File: Author Response.docx

Reviewer 2 Report

This paper used a species distribution modeling approach to model and predict the current and future potential distribution of an endangered species named Parrotia subaequalis in China. The authors then introduced the main factors influencing the species distribution and gave the response curve of this species to the three climate variables. The methodology used in this study is solid. However, my main concern is the fact that they obtained their “current climate data” from the WorldClim website and used it as a basis for modeling the current distribution of the specie in China. These data are the average of 1960-1990 as stated in the method section. The data for this period time cannot be regarded as the present climate condition as climate conditions changed considerably in these two recent decades.

My detailed comments are as follow:

 

Abstract

 

Line 17, You should indicate what does it mean DB, TM, and CC

Line 19, Did you measured fragmentation patterns or indices?

 

Introduction

Line 51, Can you mention its photosynthesis pathway?

 

Methods

Line 108, What was your criteria to consider a presence point for the species?

Line 125, The data for this period time cannot be regarded as the present climate conditions as climate condition changed considerablt in these two recent decades.

Line 162, first state the training test and then test set

 

Results

Line 211, Describe the shape of the response curves

Line 213, there is no need to report more than one decimal values.

Line 225, Remove "o"

Line 243, Have you measured fragmentation status in this area?

Line 262, Have you discussed this? reason?

Conclusion

Line 412, Have you mentioned its reason?

 

Tables

Table 3, Lowly" is not an appropriate word, Use smaller font to adjust the words and lines in this table, How it is possible that the size of suitable area for this species increase up to 2050 and then it decrease in 2070? I do not understand.

 

 

Figures

Figure 3, Write the full name of these variable in the first axes in these figures.

 

 

Comments for author File: Comments.pdf

Author Response

Dear Editor,

We would like to thank you, and the two referees for their constructive comments on previous manuscript (Forests-1853214), which is entitled “Predicting the potential distribution of endangered Parrotia subaequalis in China”. We have read and studied their comments and suggestions carefully and have made corrections which we hope meet with their approval. Please see three attachments, including a revision explanation, a revised manuscript (Word edition) and an accepted manuscript (Clean edition). The following are the correspondences to you and the reviewers concerning the comments and suggestions about the manuscript.
We submitted the original manuscript on 22nd of July, 2022. The decision from the Forests’ editorial office is “Pending major revisions”. Due to delay replying, the editor kindly suggests us to resubmit the revised manuscript.
Therefore, we have now finished resubmitting the revised manuscript. Here attached is the responding letter, revised manuscript and appendix concerning the occurrence records of Parrotia subaequalis in China.

 

To Reviewer 1
Part A: Comments from Reviewer 1
1. “The study investigates the current and future potential geographical distribution of a critically endangered plant species in China using a presence-only SDM model. I think given the fact that paper deals with an important issue i.e. a critically endangered species it worth considering publication after major changes in methodology.”
√ We express gratitude to the first reviewer for his/her critical review.

2. “Although this study is not about the modelling techniques, the authors should explain the method more clearly. How they have dealt with some uncertainty is MaxEnt model set up and data preparation such as sampling bias and variables autocorrelation. What about the suitability of correlative models for such study? Why correlative model rather that a mechanistic model as some argue that the correlation between the species and environment may cease to exist/change in future decades. All these needs to be at least discussed in introduction and discussion. Please refer to the “pdf” file for more detailed comments.”
√ Thank you very much for your comment. 
The first question is about the uncertainty of MaxEnt model in the manuscript. Firstly, we collected 211 occurrence records of Parrotia subaequalis in China. Then, we deleted incorrect or duplicate record points. Thirdly, we used the Spatially Rarefy Occurrence Data for SDMs tool (i.e. SDMtoolbox 2.0) to retain only one distribution point in each 1km × 1km grid. Thus we obtained 115 occurrence records of this species.  In doing so, this refinement of presence data will match resolution of the climate data to avoid environmental bias due to possible spatially auto-correlated presence points. Meanwhile, we also used the Spatial Analyst Tools in ArcGIS 10.6 to extract the values of 19 bioclimatic variables at these 115 distribution points. The Pearson correlation coefficient (r) between bioclimatic variables was tested by R 4.1.3 to eliminate the variables with the lower percent contribution among those | r | > 0.8.
The second question is about “the suitability of correlative models for such study”. MaxEnt model, one of the correlative models, has been widely and successfully applied to predict the distribution of rare and endangered plant species in recent years. Such examples are Trochodendron aralioides (Chiu et al., 2022), Glyptostrobus pensilis (Ye et al., 2022), and Ostrya rehderiana (Tang et al., 2022).
The third question is the reason that we selected the correlative model (i.e. MaxEnt) rather than a mechanistic model in the text. Our research material is P. subaequalis, which is an ancient tree species. It is endemic to China, and is a small deciduous tree in the family Hamamelidaceae. As a Tertiary relict plant, this species usually has a quite conservative correlation with its environment. More recently, a new study finds that the interaction between the species of Parrotia and their herbivores persisted over at least 15 million years spanning eastern Asia to western Europe (Adroit et al., 2020).
In the current study, our material is an endangered tree species endemic to China, which is totally different from pathogen (i.e. Valsa mali) or an invasive herb (i.e. Alternanthera philoxeroides) (Xu et al., 2020). For those species such as pathogen or invasive plant species, the correlation between those species and their environments may change a lot or even not exist under global change. In contrast, for Parrotia subaequalis, we do not think that such relationship may have a great change in future several decades (i.e.2050s, 2070s).
In brief, we have made some revisions in the section of Discussion according to the suggestions of the Reviewer 1. Please see Line 300-312.

References
Adroit, B.; Zhuang, X.; Wappler, T.; Terral, J.-F.; Wang, B. A case of long-term herbivory: specialized feeding trace on Parrotia (Hamamelidaceae) plant species. R. Soc. Open Sci. 2020, 7, 201449. http://dx.doi.org/10.1098/rsos.201449
Chiu, C.-A.; Matsui, T.; Tanaka, N.; Lin, C.-T. Exploring the potential distribution of relic Trochodendron aralioides: An approach using open-access resources and free software. Forests 2021, 12, 1749. https://doi.org/10.3390/f12121749 
Tang, S.L.; Song, Y.B.; Zeng, B.; Dong, M. Potential distribution of the extremely endangered species Ostrya rehderiana (Betulaceae) in China under future climate change. Environ. Sci. Pollut. Res. 2022, 29, 7782–7792. https://doi.org/10.1007/s11356-021-16268-1
Xu, W.; Sun, H.; Jin, J.; Cheng, J. Predicting the potential distribution of apple canker pathogen (Valsa mali) in China under climate change. Forests 2020, 11, 1126. https://doi.org/10.3390/f11111126
Ye, X.; Zhang, M.; Yang, Q.; Ye, L.; Liu, Y.; Zhang, G.; Chen, S.; Lai, W.; Wen, G.; Zheng, S.; et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis. Forests 2022, 13, 257. https://doi.org/10.3390/f13020257

3. “My main concern in the way that results are presented. The author refer to He et al. In categorizing the prediction. I am very surprised such an important point has not been pick up in that publication. This is a very risky and dangerous practice unless proper justifications are provided which may differ for each species. Depending on the purpose of the study, the threshold (at least the lower threshold) for this purpose can be chosen from the Table produced by MaxEnt where “Cumulative threshold”, Logistic threshold, and omission rates are provided. If one is after equal sensitivity and specificity then such threshold is selected. If one is more focused on conservation practices, the specificity is preferred etc. I suggest authors have a look at papers discussing this issue such as the following papers:
Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of biogeography, 41(4), 629-643.
Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866.(Section 4.2)
NaroueiKhandan et al. (2020). Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus. Plant Pathology, 69(1), 17-27.
(model consensus section)
Liu, Canran, Matt White, and Graeme Newell. "Selecting thresholds for the prediction of species occurrence with presence‐only data." Journal of biogeography 40.4 (2013): 778-789.
Khandan, H.A. Ensemble models to assess the risk of exotic plant pathogens in a changing climate. Diss. Lincoln University, 2014.”
√ Thank you very much for your critical comments.
First of all, I am afraid that for the Reviewer 1, there may be a misunderstanding about the citation which is mentioned in his/her comments. That is to say, “The author refer to He et al.” In fact, we did not cite this article in the original Manuscript! Furthermore, we do not know what this article (i.e. He et al.) refers to.
Currently, there is no consensus concerning the threshold of predicted suitable habitats. In the original Manuscript, we equally divided the distribution map into four categories (i.e. 0.00-0.25; 0.25-0.50; 0.50-0.75; 0.75-1.00). Such a practice is also seen in other related studies (Gülçin et al., 2021; Jiang et al., 2022). However, based on the reviewer’s advice we have carefully read the listed references and other related papers. Then we realize that it seems more appropriate to choose Max SSS (i.e. maximizing the sum of sensitivity and specificity) as the threshold of presence and absence, compared with the previous method. For rare and endangered plants, the Max SSS approach can produce higher sensitivity in most cases, and recently this practice has been used in other related studies (Xu et al., 2021; Sreekumar et al., 2022). According to the result of the MaxEnt modeling, the maximum value of sum of sensitivity plus specificity is equal to 0.112. Therefore, in the revised Manuscript we set the range from 0 to 0.1 as unsuitable habitat, and we equally divide the others into three categories including low suitable habitat (0.1–0.4), moderately suitable habitat (0.4–0.7), and highly suitable habitat (0.7–1.0).
Accordingly, we have redrawn the Figure 4 and Figure 5, and redone the Table 3. Please see more details on Line 182-189.

References
Gülçin, D.; Arslan, E.S.; Orücü, O.K. Effects of climate change on the ecological niche of common hornbeam (Carpinus betulus L.). Ecol. Inform. 2021, 66, 101478. https://doi.org/10.1016/j.ecoinf.2021.101478
Jiang, R.; Zou, M.; Qin, Y.; Tan, G.; Huang, S.; Quan, H.; Zhou, J.; Liao, H. Modeling of the potential geographical distribution of three Fritillaria species under climate change. Front. Plant Sci. 2022. 12, 749838. https://doi.org/10.3389/fpls.2021.749838
Liu, C.; Newell, G.; White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 2016, 6, 337-348. https://doi.org/10.1002/ece3.1878
Sreekumar, E.R.; Nameer, P.O. A MaxEnt modelling approach to understand the climate change effects on the distributional range of White-bellied Sholakili Sholicola albiventris (Blanford, 1868) in the Western Ghats, India. Ecol. Inform. 2022, 70, 101702. https://doi.org/10.1016/j.ecoinf.2022.101702
Xu, Y.; Huang, Y.; Zhao, H.; Yang, M.; Zhuang, Y.; Ye, X. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 2021, 12, 429. https://doi.org/10.3390/f12040429

4. “I couldn’t find supplementary files (not sure if journal asks for it or it’s there and I didn’t see it!). The presence files which include the coordinate should be included in a table with the sources that data were acquitted so other can use them if needed to replicate the study. The discussion part must be improved ( comment in pdf file(.”
√ Thank you very much for your comments. We have uploaded the coordinates of occurrence records of Parrotia subaequalis in China as an appendix. Moreover, we have also made some improvements in the section of Discussion according to the suggestions of Reviewer 1.

 

 

Part B: Notes from Reviewer 1
1. “Ln. 58-61: Re-write this, something like: 
Although the current distribution of the ... seems to be limited and narrow, discovery of new localities of P. subaequalis in Shangcheng ...... indicates that actual spatial distribution of the species be wider than its known distribution. ”
√ Thank you very much for your comment. We have accepted your suggestion and made a revision. 
Please see Line 63-69 in the revised Manuscript.

2. “Ln. 75: Delete “of all”, “accurately”.”
√ Thank you very much for your comment. We have accepted the suggestion.
 Please see Line 53, 84 in the revised Manuscript.

3. “Ln. 101: Delete “-al”. ”
√ Thank you very much for your comment. We have accepted the suggestion. 
Please see Line 111 in the revised Manuscript.

4. “Ln. 104:Delete “in”. ”
√ Thank you for your comment. 
We have accepted the suggestion, and made a revision herein.
Please see Line 114 in the revised Manuscript.

5. “Ln. 111: It is confusing the way its written. Due to resolution of the climate data, one point per pixel (~1 sq Km) we retained. ?”
√ Thank you very much for your critical review. We have accepted the suggestion, and rewritten these sentences in the section of Method. 
Please see Line 118-123in the revised Manuscript.

6. “Ln. 112: Change “Thus” to “In total”. ”
√ Thanks for your comment. We have made a revision herein.
Please see Line 123 in the revised Manuscript.

7. “Ln. 113: Change “employed” to “used”. ”
√ Thank you very much for your comment. We have changed the word “employed” to “used” herein.
Please see Line 123 in the revised Manuscript.

8. “Ln. 114-116: Does figure 2 represent all these locations?”
√ Thank you for your comment. Yes, Figure 2 represents all these locations.

9. “Ln. 116-118: Delete “These points included almost all known locations of P. subaequalis. The data files were saved as .csv format for the subsequent construction of MaxEnt model.” ”
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 126 in the revised Manuscript.

10. “Ln. 126: I wonder why the new version of the data was not used which covers 1970-2000? ”
√ Thank you very much for your comment. 
Although WorldClim 2.1 version was released in 2017 by Fick and Hijmans (2017), WorldClim 1.4 version has been widely used for species potential distribution prediction. Furthermore, it seems much convenient to compare our result of the current study with others (Ren et al., 2020; Cerasoli et al., 2022; Chen et al., 2022 ) because both use the same edition of climate data. Therefore, we chose WorldClim 1.4 version of the 1960-1990 data.
Additionally, in the original manuscript we failed to explicitly state the version of climate data. We have made revisions herein.
Please see Line 130-141 in the revised Manuscript.

References
Cerasoli, F.; D’Alessandro, P.; Biondi, M. Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe. Ecol. Evol. 2022, 12, e8430. https://doi.org/10.1002/ece3.8430
Chen, Y.; Li, Y.; Mao, L. Combining the effects of global warming, land use change and dispersal limitations to predict the future distributions of east Asian Cerris oaks (Quercus Section Cerris, Fagaceae) in China. Forests 2022, 13, 367. https://doi.org/10.3390/f13030367
Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302-4315. https://doi.org/10.1002/joc.5086
Ren, Z.C.; Zagortchev, L.; Ma, J.X.; Yan, M.; Li, J.M. Predicting the potential distribution of the parasitic Cuscuta chinensis under global warming. BMC Ecol. 2020, 20, 28. https://doi.org/10.1186/s12898-020-00295-6

11. “Ln. 133-134: Move this table (Table 1) to Results section.”
√ Thank you very much for your comment. 
We have accepted the suggestion, and moved the original Table 1 from the section of Methods to Results.
Please see Line 217 in the revised Manuscript.

12. “Ln. 144-145: Change the sentence to “and variables that contributed the most to the model gain were selected.””
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 152 in the revised Manuscript.

13. “Ln. 152: Change the sentence to “selected climate variables were ...” ”
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 159 in the revised Manuscript.

14. “Ln. 165: Change the sentence to “were used to assess the model performance. ” ”
√ We appreciate this valuable comment, and have accepted your suggestion.  
Please see Line 172-173 in the revised Manuscript.

15. “Ln. 176-177: I have noticed this kind of categorization but it should be noticed that such categorization can be mis-leading and dangerous. The values and categories should be interpreted based on model performance criteria. 
It might be acceptable to do such categorization to monitor changes under different climate scenarios but the issue should be discussed in Discussion section. Have a look at these two :
Narouei-Khandan, H. A. (2014). Ensemble models to assess the risk of exotic plant pathogens in a changing climate (Doctoral dissertation, Lincoln University).
https://doi.org/10.1111/ppa.13111 ” ” 
√ Thank you very much for your comment. This is the same question as in Comment 3. 
We have accepted your suggestion, and added a sentence in “4.4. Conservation implications for P. subaequalis” in the section of Discussion. That is “We set the cut off threshold for species presence and absence through maximizing the sum of test sensitivity and specificity, and thus such an approach can ensure the reliability of suitability classification of P. subaequalis.”
Please see more details in “Part A: Comments from Reviewer 2”.
Please see Line 182-189, 438-441 in the revised Manuscript.

16. “Ln. 188: Delete “In the meanwhile”. ”
√ Thank you very much for your comment. We have accepted the suggestion. 
Please see Line 198 in the revised Manuscript.

17. “Ln. 190: Change the sentence to “that model prediction had high.... ”. ”
√ Thank you very much for your comment. 
We have accepted your suggestion, and made a revision.
Please see Line 200 in the revised Manuscript.

18. “Ln. 199: what do you mean by “normalized”?”
√ Thank you very much for your comment. We have accepted your suggestion, and made a revision.
Please see Line 209-210 in the revised Manuscript.

19. “Ln. 204-206: No need to mention this really.”
√ Thank you very much for your comment. We have accepted your suggestion, and made a revision.
Please see Line 214-216 in the revised Manuscript.

20. “Ln. 208: Change “ existence” to “Presence”.”
√ Thank you very much for your comment. We have accepted the suggestion. 
Please see Line 221 in the revised Manuscript.

21. “Ln. 213: Mention what they are instead on codes such as precipitation in the driest quarter between .....”
√ Thank you very much for your comment. We have added the full names of these bioclimatic variables herein.
Please see Line 235-238 in the revised Manuscript.

22. “Ln. 270: This section misses important elements. Please refer to my comments. In short, the biological relevance of the variables which had the highest gain in model such as Bio01, Bio 09 and Bio17 should be discussed in more details. There should a small paragraph about the limitations of the SDMs in general or about MaxEnt itself. Please refer to the suggested publications for this purpose. 
Narouei-Khandan, H. A. (2014). Ensemble models to assess the risk of exotic plant pathogens in a changing climate (Doctoral dissertation, Lincoln University). Chapter 9 
And https://doi.org/10.1111/ppa.13111” 
√ Thank you very much for your comments. We have accepted the suggestions, and made some revisions in the section of Discussion. 
Please see Line 300-318, 324-342 in the revised Manuscript.

23. “Ln. 277: The model performed well. We can say it did well based on training data buy accuracy is has different implications. ”
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 322 in the revised Manuscript.

24. “Ln. 277-278: Change the sentence to “therefore, the model explains well the potential distribution of P. subaequalis based on current distribution. ” ”
√ Thank you very much for your comment. We have accepted the suggestion. 
Please see Line 323-324 in the revised Manuscript.

25. “Ln. 282-285: Re-write and connect it more to sound more biologically meaningful. ”
√ Thank you very much for your comment. We have accepted the suggestion, and made a revision. 
Please see Line 327-330 in the revised Manuscript.

26. “Ln. 288: Do you mean water needs? ”
√ Thank you very much for your comment. We have accepted the suggestion. 
Please see Line 333 in the revised Manuscript.

27. “Ln. 289: Change the sentence to “ experiment, Yue et al. showed that ....”. ”
√ Thank you very much for your comment. We have accepted the suggestion. 
Please see Line 334 in the revised Manuscript.

28. “Ln. 296: Change the sentence to “ merely illustrated .....”. ”
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 347 in the revised Manuscript.

29. “Ln. 303-305: I am not sure what authors means here, this issue would be a factor for all prediction models and more to consider in discussion or when one set up monitoring or response programs. ”
√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.
Please see Line 354-358 in the revised Manuscript.

30. “Ln. 308: Change “a new population locality” to “a suitable habitat”. ”
√ Thank you very much for your correction. We have accepted the suggestion.
Please see Line 366 in the revised Manuscript.

31. “Ln. 317: Change “seriously” to “extremely” .”
√ Thank you very much for your correction. We have accepted the suggestion.
Please see Line 377 in the revised Manuscript.

32. “Ln. 321,324: Delete “At the same time”, “ First of all” .”
√ Thank you very much for your comments. We have accepted your suggestions.
Please see Line 381, 384 in the revised Manuscript.

33. “Ln. 330: Change the sentence to “the species more likely ...” ”
√ Thank you very much for your comment. We have accepted the suggestion, and made a modification herein. 
Please see Line 391 in the revised Manuscript.

34. “Ln. 335-337: Not really informative and not much adding values, this could be true for many species. ”
√ Thank you very much for your comment. We have deleted this paragraph, and made a revision in the first paragraph of “4.2. Predicted habitat suitability for P. subaequalis under current scenario”.
Please see Line 354-358 in the revised Manuscript.

35. “Ln. 343, 348: Delete “Firstly” and “jointly” ”
√ Thank you very much for your comments. We have accepted the suggestion.
Please see Line 402 and 408 in the revised Manuscript.

36. “Ln. 345: Change “Secondly” to “In addition”. ”
√ Thank you very much for your comment. We have accepted your suggestion, and made a revision.
Please see Line 405 in the revised Manuscript.

37. “Ln. 349: I suggest avoid using abbreviations such as CC DB etc... in discussion so the reader can better follow the text. ”
√ We appreciate this valuable comment, and have changed the abbreviated names to full ones in the section of Discussion. 

38. “Ln. 349: Use scientific words such as insignificant or slightly, or the change was negligible ( if thats the case). ”
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 411 in the revised Manuscript.

39. “Ln. 361-362: Change the sentence to “It is plausible for ...” ”
√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.
Please see Line 426 in the revised Manuscript.

40. “Ln. 373: Delete “by”. ”
√ Thank you very much for your comment. We have accepted the suggestion.
Please see Line 439 in the revised Manuscript.

41. “Ln. 410: So many other species do the same. Not a informative... ”
√ Thank you very much for your comment. We have accepted the suggestion, and made a revision.
Please see Line 484-485 in the revised Manuscript.

 

To Reviewer 2
Part A: Comments from Reviewer 2
“This paper used a species distribution modeling approach to model and predict the current and future potential distribution of an endangered species named Parrotia subaequalis in China. The authors then introduced the main factors influencing the species distribution and gave the response curve of this species to the three climate variables. The methodology used in this study is solid. However, my main concern is the fact that they obtained their “current climate data” from the WorldClim website and used it as a basis for modeling the current distribution of the specie in China. These data are the average of 1960-1990 as stated in the method section. The data for this period time cannot be regarded as the present climate condition as climate conditions changed considerably in these two recent decades.”
√ We appreciate this critical comment. 
Regarding the “current climate data” mentioned by the Reviewer 2, our understanding is as follows. At present, the climate data downloaded from the Worldclim website have two versions including Worldclim 1.4 and 2.1, both of which can be used in MaxEnt model. WorldClim 1.4 version was released in 2005 (Hijmans et al., 2005), and WorldClim 2.1 version in 2017 (Fick and Hijmans, 2017).
In our opinion, the “current climate data” in each version is not equivalent to that of actual climate condition (i.e. “these two recent decades”). In Worldclim version 1.4, the current climate data use the average for the years 1960-1990, while in Worldclim version 2.1, the current climate data use the average for the years 1970-2000. Just as Reviewer 2 states, it is likely that the actual climatic condition has considerably changed over the past two decades. In fact, the “current climate data” in each of the two versions cannot be equivalent to the real climate data in the past two decades. Generally, the “current climate data” from the Worldclim website is only an approximation of the actual climate situation.
In this study we made prediction using MaxEnt model based on 115 distribution points of P. subaequalis and their climate data from Worldclim version 1.4 [i.e. current data from 1960 to 1990, future data in 2050s (2041-2060) and 2070s (2061-2080)]. WorldClim 1.4 version has been widely used for species potential distribution prediction. Furthermore, it seems much convenient to compare our result of the current study with others (Ren et al., 2020; Cerasoli et al., 2022) because both use the same edition of climate data.
According to the prediction results, we have delineated the possible distribution of P. subaequalis under current climate scenario, and analyzed the distribution shift in suitable areas under future climate scenarios. We think that the reliability of MaxEnt modeling depends mainly on the representation of occurrence record and climate data as well as optimization of modeling algorithm.
Firstly, the sampling data in this study came from our extensive field surveys for P. subaequalis wild populations, published literatures and related websites. We deleted incorrect or duplicate record in these obtained 211 points. Then we used the Spatially Rarefy Occurrence Data for SDMs tool (i.e. SDMtoolbox 2.0) to avoid spatially auto-correlated between presence points. Thus we got a total of 115 occurrence records of this species. The predicted distribution is consistent with the known occurrence records of P. subaequalis, which verified the sampling data’ representative.
Secondly, nowadays most scholars have distinctive views on choosing climate data from different global climate models (GCMs). For this reason, we selected the climate model data with three sets of GCMs which are most widely used (Chen et al., 2022) to increase the reliability of climate data. Accordingly, we calculated the equally-weighted mean values of Beijing Climate Center Climate System Model Version 1.1 (BCC-CSM1-1), the Community Climate System Model version 4 (CCSM4) and An Earth System Model based on the Model for Interdisciplinary Research on Climate (MIROC-ESM) to obtain a suite of future climate data.
Thirdly, we used the ENMeval package in R 4.1.3 to select the optimal model tuning parameters rather than the default parameters. Furthermore, the AUC and TSS values in this study were both greater than 0.9, indicating that the model prediction had high credibility and accuracy.

References
Cerasoli, F.; D’Alessandro, P.; Biondi, M. Worldclim 2.1 versus Worldclim 1.4: Climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe. Ecol. Evol. 2022, 12, e8430. https://doi.org/10.1002/ece3.8430
Chen, Y.; Li, Y.; Mao, L. Combining the Effects of Global Warming, Land Use Change and Dispersal Limitations to Predict the Future Distributions of East Asian Cerris Oaks (Quercus Section Cerris, Fagaceae) in China. Forests 2022, 13, 367. https://doi.org/10.3390/f13030367
Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302-4315. https://doi.org/10.1002/joc.5086
Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005, 25, 1965-1978. https://doi.org/10.1002/joc.1276
Ren, Z.C.; Zagortchev, L.; Ma, J.X.; Yan, M.; Li, J.M. Predicting the potential distribution of the parasitic Cuscuta chinensis under global warming. BMC Ecol. 2020, 20, 28. https://doi.org/10.1186/s12898-020-00295-6

Part B: Notes from Reviewer 2
Abstract
1.“Ln. 17: You should indicate what does it mean DB, TM, and CC. ”
√ We appreciate this valuable comment, and have accepted the suggestion.
Please see Line 17-18 in the revised Manuscript.

2.“Ln. 19: Did you measured fragmentation patterns or indices?”
√ Thanks for your comment. We did not measure fragmentation patterns or indices. 
Even so, our results indicated that the fragmentation degree of the Tianmu Mountain Area (TM) suitable habitat increased in the future relative to current scenario, which is shown in Figure 4 and 5. Similar to the situation in Tianmu Mountain Area (TM), the degree of habitat fragmentation in Dabie Mountain Area (DB) also showed an increasing trend under the future climate scenarios relative to current scenario.

Introduction
3.“Ln. 51: Can you mention its photosynthesis pathway? ”
√ Thanks for your comment. As far as we know, no reference states the photosynthetic pathway of P. subaequalis. We speculate that this species probably belongs to C3 pathway. However, according to the suggestion of the reviewer, we add a reference concerning its photosynthetic capacity, and made a revision herein.

Methods
4.“Ln. 108: What was your criteria to consider a presence point for the species?”
√ Thanks for your comments.
Most researchers usually obtain the occurrence point data of an endangered plant species by collecting its occurrence records from published literatures and related websites, together with some field work when they predict its potential distribution under current and future scenarios (Dhyani et al., 2021; Ye et al., 2022). In this study, we followed the methodology. That is to say, if a sample or specimen occurs in a certain site according to literatures or investigations, such a site will be initially taken as an occurrence point for this species.
Firstly, we collected occurrence points of P. subaequalis from our extensive field surveys (Li and Zhang, 2015; Zhang et al., 2016; Liu et al., 2021) and related references. After deleting incorrect or duplicate record points, we then used the Spatially Rarefy Occurrence Data for SDMs tool (i.e. SDMtoolbox 2.0) to retain only one distribution point in each 1km × 1km grid. This approach of rarefying record points is based on the resolution of bioclimatic data to reduce the spatially auto-correlated occurrence points (Radosavljevic et al., 2014; Narouei-Khandan et al., 2020). Finally, we obtained 115 occurrence points of P. subaequalis in this study.
For more details, please see “2.1 Species occurrence data” in the section of Methods.

References
Dhyani, A.; Kadaverugu, R.; Nautiyal, B.P.; Nautiyal, M.C. Predicting the potential distribution of a critically endangered medicinal plant Lilium polyphyllum in Indian Western Himalayan Region. Reg. Environ. Change 2021, 21, 30, https://doi.org/10.1007/s10113-021-01763-5
Li, W.; Zhang, G.F. Population structure and spatial pattern of the endemic and endangered subtropical tree Parrotia subaequalis (Hamamelidaceae). Flora 2015, 212, 10-18, https://doi.org/10.1016/j.flora.2015.02.002
Liu, J.; Zhang, G.F.; Li, X. Structural diversity and conservation implications of Parrotia subaequalis (Hamamelidaceae), a rare and endangered tree species in China. Nat. Conserv. 2021, 44, 99–115, https://doi.org/10.3897/natureconservation.44.69404
Narouei-Khandan, H.A.; Worner, S.P.; Viljanen, S.L.H.; van Bruggen, A.H.C.; Jones, E.E. Projecting the suitability of global and local habitats for myrtle rust (Austropuccinia psidii) using model consensus. Plant Pathol. 2020, 69, 17–27, https://doi.org/10.1111/ppa.13111
Radosavljevic, A.; Anderson, R.P. Making better MAXENT models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 2014, 41, 629–643.  https://doi.org/10.1111/jbi.12227
Ye, X.; Zhang, M.; Yang, Q.; Ye, L.; Liu, Y.; Zhang, G.; Chen, S.; Lai, W.; Wen, G.; Zheng, S.; et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis. Forests 2022, 13, 257. https://doi.org/10.3390/f13020257
Zhang, G.F.; Yao, R.; Jiang, Y.Q.; Chen, F.C.; Zhang, W.Y. Intraspecific and interspecific competition intensity of Parrotia subaequalis in different habitats from Wanfoshan Nature Reserve, Anhui Province. Chinese J. Ecol. 2016, 35, 1744–1750, https://doi.org/10.13292/j.1000-4890.201607.029

5.“Ln. 125: The data for this period time cannot be regarded as the present climate conditions as climate condition changed considerablt in these two recent decades. ”
√ Thank you very much for your comment. 
We have answered that question, please see “Part A: Comments from Reviewer 2” for details.

6.“Ln. 162: first state the training test and then test set. ”
√ Thanks for your comment. We have accepted the suggestion.
Please see Line 168-169 in the revised Manuscript.

Results
7. “Ln. 211: Describe the shape of the response curves. ”
√ Thank you very much for your comment. We have accepted the suggestion, and added the following sentences in the text:
“When precipitation of driest quarter (Bio17) exceeded 117.6 mm, P. subaequalis was in a suitable survival condition (existence probability > 0.5). With the increase of Bio17, the existence probability gradually increased and reached a peak (0.67) at 128.9 mm (Figure 3). The probability of existence then decreased in the range of 128.9-146.4 mm. Mean temperature of driest quarter (Bio9) ranged from 2.3℃ to 5.8℃, which was suitable for its growth, and the survival probability first increased and then decreased in this case, reaching the maximum at 3.1℃ (0.59). Similarly, when annual mean temperature (Bio1) was above 12.6℃, the survival probability exceeded 0.5. With the increase of Bio1, its existence probability for P. subaequalis increased to a maximum as high as 0.65 at 13.8℃.”
Please see Line 225-234 in the revised Manuscript.

8. “Ln. 213: there is no need to report more than one decimal values. ”
√ Thanks for your comment. We have accepted the suggestion.
Please see Line 225-234 in the revised Manuscript.

9. “Ln. 225: Remove "o".”
√ Thanks for your comment. I am afraid that I can not agree with you. The proportion herein refers to the proportion of the current suitable area of P. subaequalis in China's total territory. Because its value is so tiny, it is expressed as a permillage (i.e. ‰), rather than a percentage (i.e. %) in the text.

10. “Ln. 243: Have you measured fragmentation status in this area? ”
√ Thanks for your comment. This is the same question as in Note 2.
We did not measure the fragmentation status by a certain index in this area. However, we have drawn the maps of suitable habitats in this area under current and future climate scenarios. Moreover, our results indicated that the fragmentation degree of the Tianmu Mountain Area (TM) suitable habitat increased in the future relative to current scenario, which is shown in Figure 4 and 5.
According to Reviewer 2’s comment, we have made a revision herein.
Please see Line 267-268 in the revised Manuscript.

11.“Ln. 262: Have you discussed this? reason?”
√ We appreciate this valuable comment.
To date, most of endangered plants appear inconsistent trend in response to future climate change when predicting their potential habitats by MaxEnt model. To the best of our knowledge, their habitat changes can be roughly divided into three categories: (1) the increasing type, such as Thuja sutchuenensis (Qin et al., 2021); (2) the decreasing type, such as Glyptostrobus pensilis (Ye et al., 2022); (3) the fluctuating type, such as Fritillaria cirrhosa (Jiang et al., 2022).
One of the objectives in this study is “determining the responses of P. subaequalis under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) in the future (2050s and 2070s)”, rather than revealing the underlying mechanism of how it changes in different scenarios. 
According to Reviewer 2’s suggestion, we have provided a possible explanation in the text. That is: “This is consistent with the change trend of suitable habitat of three Fritillaria species (Jiang et al., 2022), which appear a trend of rising first and then declining in suitable habitats. Our results indicated that P. subaequalis may be differently adapted to the range of concentration pathways.”
Please see Line 287-290 in the revised Manuscript.

References
Jiang, R.; Zou, M.; Qin, Y.; Tan, G.; Huang, S.; Quan, H.; Zhou, J.; Liao, H. Modeling of the potential geographical distribution of three Fritillaria species under climate change. Front. Plant Sci. 2022. 12, 749838. https://doi.org/10.3389/fpls.2021.749838
Ye, X.; Zhang, M.; Yang, Q.; Ye, L.; Liu, Y.; Zhang, G.; Chen, S.; Lai, W.; Wen, G.; Zheng, S.; et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis. Forests 2022, 13, 257. https://doi.org/10.3390/f1302025
Qin, A.; Liu, B.; Guo, Q.; Bussmann, R.W.; Ma, F.; Jian, Z.; Xu, G.; Pei, S. Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch, an extremely endangered conifer from southwestern China. Glob. Ecol. Conserv. 2017, 10, 139–46. https://doi.org/10.1016/j.gecco.2017.02.004

Conclusion
12.“Ln. 412: Have you mentioned its reason? ”
√ Thanks for your valuable comment.
According to Reviewer 2’s suggestion, we have made a revision and added a reference. For more details, please see “4.2 Predicted habitat suitability for P. subaequalis under current scenario” in the section of Discussion.
Please see Line 359-362 in the revised Manuscript.

Reference
Hao, R.M.; Wei, H.T. A new combination of Hamamelidaceae. Acta Phytotaxon. Sin. 1998, 36, 80.

Tables
13. “Ln. 229: Table 3, Lowly" is not an appropriate word. ” 
√ Thanks for your correction. We have accepted the suggestion. 
Please see Line 253 (Table 3) in the revised Manuscript.

14. “Ln. 229: Table 3, Use smaller font to adjust the words and lines in this table. ” 
√ Thanks for your comment. We have accepted your suggestion, and made a modification.
Please see Line 253 (Table 3) in the revised Manuscript.

15. “Ln. 229: Table 3, How it is possible that the size of suitable area for this species increase up to 2050 and then it decrease in 2070? I do not understand. ”
√ Thank you very much for your comment. 
This is the same question as in Note 11. Please see our reply to Note 11.

Figures
16. “Ln. 217: Figure 3, Write the full name of these variable in the first axes in these figures.”
√ Thank you very much for your critical review. We have accepted the suggestion. 
Please see Line 242 in the revised Manuscript.

 

 

To Editor (Alina Li)
√ We have accepted the suggestions and made revisions accordingly in the revised Manuscript.

Besides, we have made further modifications after carefully checking the whole manuscript. They are as follows.
√ We have recalculated the data in the manuscript and redrawn Figure 4 & 5.
√ We have added thirteen references and deleted two references.
√ We have checked the manuscript thoroughly in order to make sure the formats meet the requirements of Forests.

On behalf of all authors, I hope that the revised manuscript will be acceptable for publication in Forests.
If you have any question about this paper, please do not hesitate to contact me.

Best regards,
Guangfu Zhang
2022/9/20

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have addressed the major points, but some parts need change of language that I have commented on the pdf file attached.  I am glad that authors have explored to see the best cut-off threshold and since this study has conservation focus, one is intreated in specificity more than sensitivity. 

The changes are minor, so the manuscript doesn’t need to get back to me. If the editor believes the changes are made then its publishable in my opinion.  

Comments for author File: Comments.pdf

Author Response

Dear Editor,

 

We would like to thank you, and the two referees again for their constructive comments on previous manuscript (Forests-1853214), which is entitled “Predicting the potential distribution of endangered Parrotia subaequalis in China”. We have read and studied their comments and suggestions carefully and have made corrections which we hope meet with their approval. Please see three attachments, including a revision explanation, a revised manuscript (Word edition) and an accepted manuscript (Clean edition). The following are the correspondences to you and the reviewers concerning the comments and suggestions about the manuscript.

 

To Reviewer 1

 

Notes from Reviewer 1：

1. “Ln. 59: Comma after species name.”

√ Thank you very much for your comment. We have accepted your suggestion and made a revision.

Please see Line 55 in the revised Manuscript.

 

2. “Ln. 68: Identifying new localities .....Use identifying instead of "discovery".”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 64 in the revised Manuscript.

 

3. 3. “Ln. 221-227: The categories also include unsuitable habitat so this is not quite true. Write instead:

The models results were classified based on "maximizing .......threshold" given the nature of the studied species.”

√ Thank you very much for your comment. We have accepted your suggestion and made a revision.

Please see Line 183-185 in the revised Manuscript.

 

4. 4. “Ln. 269: Provided or calculated instead of obtained.”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 210 in the revised Manuscript.

 

5. 5. “Ln. 275: Variables instead of indices.”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 216 in the revised Manuscript.

 

6.“Ln. 288:Change “ Figure 3” to “Figure 3a”.. ”

√ Thank you for your comment.

We have accepted the suggestion, and made a revision herein.

Please see Line 226, 233 and 236 in the revised Manuscript.

 

7.“Ln. 144-145: Change the sentence to “......increase of Bio1, the probability of P. s establishment increased to......””

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 235-236 in the revised Manuscript.

 

8.“Ln. 308:Change “ Figure 3” to “Figure 3c”. ”

√ Thank you for your comment.

We have accepted the suggestion, and made a revision herein.

Please see Line 236 in the revised Manuscript.

 

9.“Ln. 494-496: Mechanistic models. This is a claim that is hard to prove. You can say that it is claimed that correlative models ..... And then cite the relevant papers. If not delete the sentence. . ”

√ Thank you very much for your comment. We have accepted the suggestion, and deleted this sentence herein.

Please see Line 303 in the revised Manuscript.

 

10.“Ln. 499-500: Change the sentence to “such as its ability to work with small sample size, its ease of use and claimed superior performance.””

√ Thank you very much for your comment.

We have accepted your suggestion, and made a revision.

Please see Line 306-307 in the revised Manuscript.

 

11.“Ln. 506: Remove, not informative, this is something that almost all algorithms do. ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a revision herein.

Please see Line 313 in the revised Manuscript.

 

12.“Ln. 513: what are these? Write in full.”

√ Thank you very much for your comment. We have accepted your suggestion.

Please see Line 321 in the revised Manuscript.

 

13.“Ln. 527: year?”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 335 in the revised Manuscript.

 

To Editor (Alina Li)

√ We have accepted all suggestions from the Reviewer 1 and made revisions accordingly in the revised Manuscript.

 

Besides, we have made further modifications after carefully checking the whole manuscript.

 

On behalf of all authors, I hope that the revised manuscript will be acceptable for publication in Forests.

If you have any question about this paper, please do not hesitate to contact me.

 

Best regards,

Guangfu Zhang

2022/9/23

 

Author Response File: Author Response.docx

Reviewer 2 Report

The authors made substantial changes to the manuscript and I believe it can be accepted in its current form.

Author Response

Dear Editor,

 

We would like to thank you, and the two referees again for their constructive comments on previous manuscript (Forests-1853214), which is entitled “Predicting the potential distribution of endangered Parrotia subaequalis in China”. We have read and studied their comments and suggestions carefully and have made corrections which we hope meet with their approval. Please see three attachments, including a revision explanation, a revised manuscript (Word edition) and an accepted manuscript (Clean edition). The following are the correspondences to you and the reviewers concerning the comments and suggestions about the manuscript.

 

To Reviewer 1

 

Notes from Reviewer 1：

1. “Ln. 59: Comma after species name.”

√ Thank you very much for your comment. We have accepted your suggestion and made a revision.

Please see Line 55 in the revised Manuscript.

 

2. “Ln. 68: Identifying new localities .....Use identifying instead of "discovery".”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 64 in the revised Manuscript.

 

3. 3. “Ln. 221-227: The categories also include unsuitable habitat so this is not quite true. Write instead:

The models results were classified based on "maximizing .......threshold" given the nature of the studied species.”

√ Thank you very much for your comment. We have accepted your suggestion and made a revision.

Please see Line 183-185 in the revised Manuscript.

 

4. 4. “Ln. 269: Provided or calculated instead of obtained.”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 210 in the revised Manuscript.

 

5. 5. “Ln. 275: Variables instead of indices.”

√ Thank you very much for your comment. We have accepted the suggestion.

 Please see Line 216 in the revised Manuscript.

 

6.“Ln. 288:Change “ Figure 3” to “Figure 3a”.. ”

√ Thank you for your comment.

We have accepted the suggestion, and made a revision herein.

Please see Line 226, 233 and 236 in the revised Manuscript.

 

7.“Ln. 144-145: Change the sentence to “......increase of Bio1, the probability of P. s establishment increased to......””

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 235-236 in the revised Manuscript.

 

8.“Ln. 308:Change “ Figure 3” to “Figure 3c”. ”

√ Thank you for your comment.

We have accepted the suggestion, and made a revision herein.

Please see Line 236 in the revised Manuscript.

 

9.“Ln. 494-496: Mechanistic models. This is a claim that is hard to prove. You can say that it is claimed that correlative models ..... And then cite the relevant papers. If not delete the sentence. . ”

√ Thank you very much for your comment. We have accepted the suggestion, and deleted this sentence herein.

Please see Line 303 in the revised Manuscript.

 

10.“Ln. 499-500: Change the sentence to “such as its ability to work with small sample size, its ease of use and claimed superior performance.””

√ Thank you very much for your comment.

We have accepted your suggestion, and made a revision.

Please see Line 306-307 in the revised Manuscript.

 

11.“Ln. 506: Remove, not informative, this is something that almost all algorithms do. ”

√ Thank you very much for your comment. We have accepted the suggestion, and made a revision herein.

Please see Line 313 in the revised Manuscript.

 

12.“Ln. 513: what are these? Write in full.”

√ Thank you very much for your comment. We have accepted your suggestion.

Please see Line 321 in the revised Manuscript.

 

13.“Ln. 527: year?”

√ Thank you very much for your comment. We have accepted the suggestion.

Please see Line 335 in the revised Manuscript.

 

To Editor (Alina Li)

√ We have accepted all suggestions from the Reviewer 1 and made revisions accordingly in the revised Manuscript.

 

Besides, we have made further modifications after carefully checking the whole manuscript.

 

On behalf of all authors, I hope that the revised manuscript will be acceptable for publication in Forests.

If you have any question about this paper, please do not hesitate to contact me.

 

Best regards,

Guangfu Zhang

2022/9/23

 

Author Response File: Author Response.docx