Modeling the Effect of Climate Change on the Potential Distribution of Qinghai Spruce (Picea crassifolia Kom.) in Qilian Mountains
Round 1
Reviewer 1 Report
The paper is well written and the logic is easy to follow. They try to understand what’s the effect of the climate change on the potential distribution of Qinghai Spruce on Qilian Mountains. This issue has great social and eco- significance. The analysis is very thorough and solid, but there is also some environment factors or potential influences, which are not considered well in the study. Overall, it is a very good study and should be accessed to broad reader community. I only have two minor concerns on this issue.
1. To consider the environmental data, mainly the temperature information is considered, but different models may show different temperature changes, especially at the regional scale, as have been shown in Zhou et al. (2013). Zhou et al. (2013) found that different models and different realizations in the same model may show quite different historical temperature evolution. This should also be right for the future projection. The potential effect on the main results should be discussed.
2. The different temperature projection may be related to the atmospheric circulation structure change, which is internal and quite independent of climate change. Over Qilian Mountains regions, it is mainly dominated by westerly jet and so-called “silk-road” pattern along the westerly jet is a dominant atmospheric circulation system as revealed by Song et al. (2013) and Song and Zhou (2013). The potential difference of temperature projection should be related to this circulation change. This should also be discussed in the paper.
Kosaka, Y., H. Nakamura, M. Watanabe, and M. Kimoto, 2009. Analysis on the dynamics of a wave-like tele- connection pattern along the summertime Asian jet based on a reanalysis dataset and climate model simulations. J. Meteor. Soc. Japan, 87, 561–580.
Song, F. F., T. J. Zhou, and L. Wang, 2013. Two modes of the Silk Road pattern and their interannual variability simulated by LASG/IAP AGCM SAMIL2.0. Adv. Atmos. Sci., 30(3), 908–921, doi: 10.1007/s00376-012-2145-1.
Author Response
Dear reviewer:
All authors in the manuscript appreciate your comments and suggestions. We have done our best
to revise the manuscript according to these valuable comments.
After several days' efforts, we finally completed the revision of the paper. Please refer to the attachment for details.
Thank you for your consideration!
Sincerely,
Chuanyan Zhao, PhD
Professor, College of Pastoral Agriculture Science and Technology
Lanzhou University, China
Author Response File: 
Author Response.docx
Reviewer 2 Report
The authors proposed a simulation of the climate change impacts on the distribution of Qinghai Spruce in the Qilian Mountains.
Overall, the article is hard to read and I recommend the authors to carefully re-read it and rephrase some parts. Most tables are very big and can be synthesized in figures (tables can be added as annex), and figure 2 is too small.
The study uses a model already developed with its default settings (Maxent). A more detailed description of this model can be useful for the reader. Some points are addressed in the introduction, but without explanation (for instance, I would like to know why Maxent can handle small sample size, line 57). Moreover, the accuracy of the model is based on the AUC, and judged as excellent, but a more detailed explanation of this judgment is missing (line 133-138).
Climate data are downloaded from the WorldClim data set, and are not downscaled for the region of interest (unless not specified in the paper). The variable selection used to select the 7 variables of interest is based only on the collinearity of the data, if two data have a Pearson correlation>0.8, only one was selected, but there is no details about how the authors selected one variable more than the other. Moreover, table 2 doesn’t allow a clear idea of the correlation between variables. A PCA might be useful to better understand how the climate variables are correlated, and to choose variables that are not only independent, but also have high scores on the principal axis. Moreover, a description of the environmental variables and the expected impacts on the distribution of Qinghai Spruce is missing (for instance, is precipitation only rain, or also snow? How are the driest and the wettest season identified? Why are these variables tested?). Another better option would be to use a model to compute water availability from the different available variables.
The four RCP scenarios are used to create four simulations, and variables contributions to the Qinghai spruce distribution are analyzed. However, the contribution of each of these variables to the climate variability for the four scenarios is not showed or mentioned. Therefore, it is hard to know if a variable has little impact on the Qinghai spruce distribution, or if it is just not changing for the four scenarios.
The discussion and conclusion repeat most of the results, but do not discuss the impact of each selected variable from a physiological point of view, nor the limits of the model used (Maxent). The divergent results mentioned line 272-273 are not discussed further (I did not understand the sentence line 274-275). The difference between the fundamental niche and the realized one is mentioned only shortly at the end of the paper, and no further development of the model are proposed, for instance to take the intensive deforestation into account.
Author Response
Dear reviewer:
All authors in the manuscript appreciate your comments and suggestions. We have done our best
to revise the manuscript according to these valuable comments.
After several days' efforts, we finally completed the revision of the paper. Please refer to the attachment for details.
Thank you for your consideration!
Sincerely,
Chuanyan Zhao, PhD
Professor, College of Pastoral Agriculture Science and Technology
Lanzhou University, China
Author Response File: Author Response.docx
