Predicting Climate Change Impacts on Candelilla (Euphorbia antisyphilitica Zucc.) for Mexico: An Approach for Mexico’s Primary Harvest Area

Round 1

Reviewer 1 Report

Dear Authors,

 You have interesting research or work.

 I have one point about your work and it is 8 Figures Maps are not the best quality. you would modify it.

 

Best regards ...

 

Author Response

REVIEWER 2: You have interesting research or work.  I have one point about your work and it is 8 Figures Maps are not the best quality. you would modify it.

AUTHORS: The 8 figures in the pdf version reduce their quality, however, the word version retains a good quality.

Author Response File: Author Response.pdf

Reviewer 2 Report

The name of plant is not italicized in title. 

In Abstract, the conclusion line must be more precise. her eit is more general.
It is better if authors add some detail of biodiversity in maxico ther ein first paragraph of introduction
In introduction the line "Candelilla wax generated profits of just over USD $4.8 million as a result of selling 1,311 50 tons in 2015"  the data is of 2015. Some fresh data should be aded.
the methodologya nd results are nicely described here. However in discussion some improvement is required. The parameters must be  discussed with respect to previously studied models from literature other then the MPIESM-LR and HadGEM2-ES models.
After these modifications paper can be accepted.

Author Response

REVIEWER 2: The name of plant is not italicized in title.

AUTHORS: the name of the species was changed to italics

 

REVIEWER 2: In Abstract, the conclusion line must be more precise. her eit is more general.

AUTHORS: The conclusion section of the abstract (L 30-35) is more detailed.

 

REVIEWER 2: It is better if authors add some detail of biodiversity in maxico ther ein first paragraph of introduction.

AUTHORS: Biodiversity information was included with some of the most important species of the Chihuahuan Desert in northern Mexico (L 43-46)

 

REVIEWER 2: In introduction the line "Candelilla wax generated profits of just over USD $4.8 million as a result of selling 1,311 50 tons in 2015"  the data is of 2015. Some fresh data should be aded.

AUTHORS: Updated to the latest figure obtained from Mexico's national statistics (2019).

 

REVIEWER 2: the methodologya nd results are nicely described here. However in discussion some improvement is required. The parameters must be  discussed with respect to previously studied models from literature other then the MPIESM-LR and HadGEM2-ES models.

AUTHORS: The discussion section was complemented with other studies using other models (L 599- 604).

Reviewer 3 Report

Dear Authors,

Optimal management strategy is essential to harvest the Candelilla produce sustainability and its importance in addressing climate change issues in the study area.  The manuscript presents a new data processing method that provides accurate information about these resources and their forecasting, which is crucial in achieving the aforementioned goals. The work is invaluable and both academic and practical relevance can be found in the problem choice and importance.

 

The manuscript presents a new data processing method that provides accurate information about these resources and their forecasting, which is crucial in achieving the aforementioned goals. The work is invaluable, and academic and practical relevance can be found in the problem choice and importance.

The manuscript is well-written and grammatically sound. The literature was reviewed appropriately and effectively, and the research objectives and title are justified by the adequate and conventional methods utilized. Data were collected methodically, in accordance with acceptance criteria. The data was collected in a proper and sufficient manner, and the data analysis and interpretation are appropriate and relevant to the objectives. The results were presented and discussed well, with suitable tables and figures, and relevant updated literature. Scientific terms were cited and measurements were properly accounted for.

 Overall, I congratulate the authors on their excellent work and wish them continued success in contributing to society through their research.

There is scope to improve the manuscript Below given are my observations, Kindly incorporate minor suggested changes. 

Thanks and reagrds 

 

Point 1	Scope to improve research gaps and research contribution
Point 2	Scope to improve the value of the study
Point 3	Scope to improve the practical application for the implication
Point 4	Please explain the limitation of the study, and future research direction more clearly.

Author Response

Reviewer: Scope to improve research gaps and research contribution

Authors: The original contribution of the study is found in lines 642-650

 

Reviewer: Scope to improve the value of the study

Authors: the value of the study is presented on lines 68-84

 

Reviewer: Scope to improve the practical application for the implication

Authors: The practical contribution is in lines 650-652

 

Reviewer: Please explain the limitation of the study, and future research direction more clearly.

Authors: The limitation and orientation towards the following research in lines 652-655 were included

Reviewer 4 Report

The authors sought to predict climate change impacts on candelilla (Euphorbia antisyphilitica Zucc.) for Mexico. They used a robust dataset and method. There are, however, just a few clarifications that are needed before I can recommend this paper for publication.

 

Specifically, lines 165 to 173. I am not sure how this method works and how the authors were able to identify which of the correlated predictors should be selected based on their importance first and then their correlation with other predictors (this is the order by which the elimination process should proceed). That is, how did they decide that for example BIO7 was more important than other correlated predictors and hence was chosen and predictors significantly correlated to it were removed. I had to try to find the cited source of the method, one of which was written in Spanish and another source does not specify how this can be done (outside of using feature selection algorithms, correlation analysis, clustering algorithms, principal components analysis or some other dimension reduction method).

Was the ENMeval library used, if so which function in the library? In a nutshell, more information is needed. Please state how the importance of each variable was first determined before correlated variables were then identified and eliminated.

 

Suggested corrections include:

Line 21: The sentence is incomplete. I would suggest adding “were used in our analysis.” Or “were used to create species distribution models” after “topography variables”. Also, perhaps “topography” should be changed to “topographic”.

Line 71: Change “has” to “have”.

Line 141: Why was this period used. What was the range of collection dates for the presence data? Please state this in your method as this will determine if this period was adequate.

Line 161: Please state what the acronym represents here first and not in line 166.

Author Response

Reviewer: Specifically, lines 165 to 173. I am not sure how this method works and how the authors were able to identify which of the correlated predictors should be selected based on their importance first and then their correlation with other predictors (this is the order by which the elimination process should proceed). That is, how did they decide that for example BIO7 was more important than other correlated predictors and hence was chosen and predictors significantly correlated to it were removed. I had to try to find the cited source of the method, one of which was written in Spanish and another source does not specify how this can be done (outside of using feature selection algorithms, correlation analysis, clustering algorithms, principal components analysis or some other dimension reduction method).

Authors: the study by Merow, 2013 mentions the following:

“We recommend that users minimize correlation among predictors and identify the appropriate feature shapes prior to model building (depending on study goal). From the collection of biologically plausible predictors, we recommend removing highly correlated predictors using correlation analysis, clustering algorithms, principal components ana lysis or some other dimension reduction method because the complex features created by MaxEnt are often already highly correlated”.

[35] Merow, C.; Smith, M.J.; Silander, J.A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 2013, 36 (10), 1058-1069.  https://doi.org/10.1111/j.1600-0587.2013.07872.x

To improve the wording of the methodology, it was included that the selection of variables was made with Pearson bivariate correlation

 

Reviewer: Was the ENMeval library used, if so which function in the library? In a nutshell, more information is needed. Please state how the importance of each variable was first determined before correlated variables were then identified and eliminated.

Authors: The reviewer's request was included, the usefulness of the ENMeval library was described, and the variable selection process was added.

 

Reviewer: Line 21: The sentence is incomplete. I would suggest adding “were used in our analysis.” Or “were used to create species distribution models” after “topography variables”. Also, perhaps “topography” should be changed to “topographic”.

Authors: corrected as suggested by the reviewer

 

Reviewer: Line 71: Change “has” to “have”.

Authors: Done

 

Reviewer: Line 141: Why was this period used. What was the range of collection dates for the presence data? Please state this in your method as this will determine if this period was adequate.

Authors: the period 1970-2000 is the period used by Fick and Hijmans to develop the WorldClim version 2.0 global bioclimatic variables, this period cannot be chosen based on the records, it is determined.

 

Reviewer: Line 161: Please state what the acronym represents here first and not in line 166.

Authors: Done

 

Round 2

Reviewer 4 Report

After reviewing the revisions, this paper is suitable for publication. Just a few modifications that can be made before publication. Specifically, my suggested rewriting of lines 165 - 171. You can edit as deemed appropriate. 

The calibration was developed with the ENMevalate function of the ENMeval library implemented using the R programming language [33]. The function iteratively creates ecological niche models through a variety of fit configurations and allows for model evaluation using cross-validation or a fully retained test data set [32]. The function returns evaluation statistics for each combination of configuration and cross-validation fold, as well as raster predictions for each model when raster data are entered [32]. The evaluation statistics, which is returned in a table, allows for the identification of  a model configuration that balances fit and predictive ability [32].

 

Author Response

Reviewer: After reviewing the revisions, this paper is suitable for publication. Just a few modifications that can be made before publication. Specifically, my suggested rewriting of lines 165 - 171. You can edit as deemed appropriate.

 

The calibration was developed with the ENMevalate function of the ENMeval library implemented using the R programming language [33]. The function iteratively creates ecological niche models through a variety of fit configurations and allows for model evaluation using cross-validation or a fully retained test data set [32]. The function returns evaluation statistics for each combination of configuration and cross-validation fold, as well as raster predictions for each model when raster data are entered [32]. The evaluation statistics, which is returned in a table, allows for the identification of  a model configuration that balances fit and predictive ability [32].

 

Authors: paragraph was modified as suggested by the reviewer