Prediction of Potential Habitat of Monochamus alternatus Based on Shared Socioeconomic Pathway Scenarios

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

The manuscript titled “Prediction of potential habitat of Monochamus alternatus based on SSPs scenario” modeled the potential suitability areas for Monochamus alternates, a vector species of a nematode parasite using distribution points and environmental variable and also using different models under both current and future climate scenarios. The study succeeds in identifying the environmental variables and prediction of habit suitability in some future climatic scenarios. Overall, this research is potentially full of interest, as it addresses the relevant topic. I have few comments and suggestions.

Major issues

1.      The literature review section is not sufficient. Most of the Introduction and discussion section moves around software composition, working and models output. The literature review can help identify the contradictory thoughts, perspectives, and theoretical implications (if any) while also scrutinizing the gap in the present research. How does current research comply or contradict the earlier thoughts? The literature in the introduction and discussion sections can then support the LR to build a case for the reader to understand the research problem statement precisely. Insertion of a new paragraph that relates this work with the previous similar observation is highly recommended. 

2.      I am unable find your field data points from the distribution range as you only collected data from occurrence points of the vector insect, from the GPS points provided by the KOFPI. However, for better accuracy and predictability, data should be supplemented with intensive field surveys by the author’s alteast from some prominent Provinces like Gyeongnam Gyeongbuk Gwangju etc. Moreover, it is a well-known fact that distribution data is frequently skewed toward different geographical locations that may or may not be easily accessible.

 

3.      I suggest the authors to analyze species range change of the insect vector in different future climatic scenarios using Biomod2 ensemble approach. 

Minor issues 

Line 21-23, the last sentences of the abstract should be the conclusion and future implications of the research work.

There is a mention of two insect vectors responsible for PWD and you only choose Monochamus alternates, there are chances that the potential risk of the other is underestimated. 

Please elaborate EIs in subheading 2.1.2, 2.1.4, and 2.2. 

Section 3.2.2. Evaluating Accuracy needs to be supplemented with a suitable diagram depicting model accuracy.

 Wish you luck

 

Author Response

Thank you so much for your thoughtful comments. Please check the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The longhorn beetles of the genus Monochamus are well known as important pests of forests, especially the coniferous one. However, at least several species of this genus may serve as main vectors for another extremely important pest, namely the roundworm Bursaphelenchus xylophilus. In Eurasia, the outbreaks of this nematode started several decades ago after its invasion. In this context, some forecasts of the beetle distribution become very important for now and for the future. The authors tried to use some actual data and the SDMs and to predict possible shifts in distribution of M. alternatus in the Republic of Korea.

I am sure that studies are original and important, particularly for applied forest entomology. Besides, some ideas of the authors may be useful for development of the SDM, especially when several different modelling approaches may be used.  

However, there are some important issues:

(1) In the Republic of Korea, there are at least two other species of this genus, namely M. urussovi and M. nitens, and both may serve as the vectors of this nematode. Unfortunately, the authors didn't mention them.

(2) Please, check all parts where you noted the resolution level. The problem is that the main databases of environmental variables include data associated with geographic coordinates. I believe you used commonly the data with spatial resolution at 30 arcsecond. Actually, in the Republic of Korea, the plot 30 x 30 arcsecond equals approximately 0.926 x 0.758 m at sea level (not 1 x 1 km). However, some problems may arise if you used different types of data (e.g. with resolution at 30 arcsecond and with true resolution at 1 km). In this case, the data with different resolution should be recalculated.

(3) Lines 83–87 — please, clarify the situation, because some readers may understand that you used the points of PWD damage, not data on the distribution of M. alternatus per se.

(4) You used one or several climatic models to produce the SDMs for the future periods, but did not explain what climatic model(s) was(were) used... Unfortunately, now we have more than 100 global climatic models, and forecasts based on 14 models are included in the WorldClim database. Our experience shows that selection of climatic models may affect significantly results of SDM. Please, clarify this part of your manuscript.

(5) If I understand that right, the maximum entropy models produced included data on forest cover. But what data were really used? Tree species distribution? NPP? Some other forest characteristics? And how did you take into account possible alterations of forests in the future? They may significantly shift due to both climate changes and changes of human activity... Besides, some comparative analysis of models may be done if the models are based on the same set of independent parameters.

(6) Subsection 2.2.2 — Why did you use this set of the models and why did not include in this set the MaxEnt?

(7) lines 392–400 — maybe, the MaxEnt doesn't overestimate some trend, but 'ensemble' underestimates them?

The conclusions are formally consistent with evidence and arguments. However, some issues (points 2–5) may affect the results.  

Technical comments

Please, edit the title — maybe so: Prediction of potential habitats of Monochamus alternatus based on the Shared Socioeconomic Pathway scenarios

lines 51–52 — the SDMs may be based on occurrence points only or on occurrence/absence points or on data describing population characteristics or on data characterizing environmental characteristics of a species...  

lines 75, 86 and so on — please, try to use "the points with geographic coordinates" (or something similar) instead of GPS points.

line 87 – valuables > variables

Table 1 and so on — 3,902 > 3902 (but 20,514!)

line 123 — if you used the Pearson correlation, you had to check the data normality. Please, comment the situation.

line 125 and so on — please, try to use in the text (1) the real name of variables (e.g. annual precipitation) and to use the abbreviations in the uniform manner (e.g. line 125 — BIO1, line 314 — bio1, Table B1 — Bio1).  

line 286 and so on — please, include km² as in the main font of the text

lines 352 and 353 — please, don't repeat the same data

line 403 — these species or this species?

 

Comments on the Quality of English Language

The quality of English language is applicable.

Author Response

Thank you so much for your thoughtful comments. Please check the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors,

Thank you very much for significant improvements of your text. However, I hope two important issues should be clarified.

(1) You forecast some possible changes of the model species distribution for several periods in the future based on the SSP scenarios. However, the SSPs predict only very general trends, mainly average global temperatures, this is why the models of global climatic changes produced in the frames of  the World Climate Research Programme (WCRP) (e.g., ACCESS-CM2, GISS-E2-1-G, MRI-ESM2-0 etc.) are commonly used. The results of some model predictions were used to produced geotiff files those may be used for the species distribution modelling for the future periods. The question is what the climatic model(s) did you use to generate the SDMs for the future periods?

(2) You used the Pearson correlation coefficient. However, this coefficient may be used only for the data with normal (Gaussian) distribution. This means you should check the statistical distribution of your original data. If your data are not distributed normally (i.e., they are non-parametric), some non-parametric techniques should be used, e.g. the Spearman rank-order correlation. Please, clarify this part of your studies.

Comments on the Quality of English Language

The quality of English language is applicable.

Author Response

Please see the attachment.

Author Response File: Author Response.docx