the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2021
A biomass equation dataset for common shrub species in China
Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.
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CC1: 'Comment on essd-2021-44', Dayong Fan, 06 Aug 2021
In the present study, the authors developed the first biomass equation dataset for common shrub species in China. This impressive dataset is systematic and comprehensive, providing the critical base for the terrestrial ecosystem biomass and carbon sink capacity evaluation in China for future studies.
Here I have some comments:
- The dataset needs more clarifications. 1) in the “Description” page, there are two identical symbols named “n” with different meanings, and two identical symbols named “R2[R]” with different meanings. 2) in the “General” page, what do the “xx” and “mx” mean? 3) in the “Equation” page, symbol “H”, “C”, “Ac”, “D”, and “M” have not been defined. 4) the year of the raw data acquired should be mentioned for each equation. 5) the longitude and latitude, or the longitude and latitude range in which the raw data was collected and corresponding equation was generated, should be provided. The last two points are important, because the journal has one principle for the data description: “Specify the temporal and geographical scopes, and temporal and spatial resolutions of your data wherever appropriate”.
- The text to explain the dataset needs some revisions. 1) line 40, “Representative researches” should be “Representative researches in China”. 2) line 100, it is good if a schematic diagram could be presented to show the difference among the three types of shrub species. 3) the style of parameters in functions should be 4) line 148, the word “Consequently” can be replaced by “Collectively”. 5) Line 176, “The sample size varied from 5 and 312 shrubs”, this means in some samples data cannot be split into 10% and 90%, and the 90% part cannot be split into 75% and 25% for accuracy test. How many of them? And what’s the lowest limit of the sample size which can satisfy the statistical requirement of the recommended method? 6) line 183 and later, “valve” should be “value”.
Citation: https://doi.org/10.5194/essd-2021-44-CC1 -
AC1: 'Reply on CC1', Yang Wang, 17 Oct 2021
Thank you very much for your detailed suggestions. The specific revisions are as follows:
1、In the Description sheet, the first “n” is the number of shrub samples used in equation creation, we revised it to “n in equation creation”; the second “n” is the number of shrub samples used in equation evaluation, we revised it to “n in equation evaluation”.
The first “R2[R]” is the goodness-of-fit statistics used in equation creation, we revised it to “R2[R] in equation creation”; the second “R2[R]” is the equation evaluation statistics used in equation evaluation, we revised it to “n in equation evaluation”.
2、In the General sheet, we revised “xx” and “mx” to “inf” and “equ” which are short for “information” and “equation”, respectively.
3、In the Equation sheet, we clearly illustrated the meaning of symbols in “Remarks” column, include “H”, “C”, “Ac”, “Vc”, “D”, “D10”, “P”, “N”, “Ma” and “M”.
4、Field measured data of shrubs were obtained from 2011 to 2013, we added this information in the Introduction sheet instead of demonstrating it for each equation.
5、In the General sheet, we added the longitude and latitude, or the longitude and latitude range in which the raw data was collected and corresponding equation was generated.
6、We revised the text according to your suggestions in line 40, line148 and line183.
7、In some cases, equations with small sample sizes lack validation, they are all from the literature. The lowest limit of the sample size of each shrub species obtained from field measurement is 16.Citation: https://doi.org/10.5194/essd-2021-44-AC1
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CC2: 'Comment on essd-2021-44', Xiangping Wang, 07 Aug 2021
This manuscript (MS) reports a large dataset for allometric equations to estimate shrub biomass, for a variety of species and sites across China. Interestingly, it seems that most equations were constructed by the present study based on sampling of 738 sites using a unified method. This greatly improved the quality and comparability of the equations, which avoid the weakness of compiling data from literatures (which were generally somehow different in sampling methods and thus led to uncertainty in data quality). Consequently, the dataset is clearly useful for improving the carbon pool/sink estimation of shrub ecosystems.
Considering the importance of this dataset, here I have some suggestions for the authors to improve the MS:
1) The Methods section needs to be clearer. As mentioned above, a major advantage of the dataset is that they have many equations based on their own measurements (the abstract said that they have 738 sites). However, the methods to obtain these equations were introduced together with the equations complied from literatures. This leads the readers not very clear about the details of the methods. For instance, did they obtain one or more equations for each of the 738 sites? How many equations from the 822 equations were measured by this study? In my opinion, similar methods issues are better introduced independent of the equations collected from the literatures.
2) As for the equations from the literatures, these are also good data in supplementary to the measured equations. However, the methods to compile, select and validate equations from literatures clearly are different, and may be better to be introduced in another section.
3) In the Excel file reporting the data, I suggest to add a column in the “Equation” sheet, which clearly indicate the data source of the equation (e.g. “this study” or “the reference”). Presently, this information is reported in the “General” sheet. This is not convenient for the readers (personally I would prefer the equations measured by the present study, as explained above). Meanwhile, I also suggest the ranges of shrub height, crown, diameter etc. to be given for each equation in the “Equation” sheet. These ranges are critical for readers to determine whether an equation can be used for their estimation. However, these ranges are now only given for each species, which is not convenient for the readers.
4) The abbreviations in the “Equation” sheet seemed not well described. For instance, what does Vc, Ma, Ac, N, etc. mean? I did not find these in the “Description” sheet. Did I miss something?
Citation: https://doi.org/10.5194/essd-2021-44-CC2 -
AC2: 'Reply on CC2', Yang Wang, 17 Oct 2021
Thank you very much for your revision suggestions.
1、In the “Materials and methods” section, methods of equation collection from the literatures were illustrated in “2.1 Literature retrieval” and “2.2 Equation collection and screening”. Methods to create equations with field measured data were illustrated in “2.3 Equation creation and evaluation”.
2、In the “Equation” sheet, we arranged information most directly related to equations, such as predictor variables, equation forms, equation coefficients and statistical parameters in equation evaluation, etc. Therefore, other information not most directly related to equations can be found in the “General” sheet through retrieval.
3、Description of abbreviations such as “H”, “C”, “Ac”, “D”, “M” in the “Equation” sheet were added in the “Description” sheet.Citation: https://doi.org/10.5194/essd-2021-44-AC2
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AC2: 'Reply on CC2', Yang Wang, 17 Oct 2021
Status: closed
-
CC1: 'Comment on essd-2021-44', Dayong Fan, 06 Aug 2021
In the present study, the authors developed the first biomass equation dataset for common shrub species in China. This impressive dataset is systematic and comprehensive, providing the critical base for the terrestrial ecosystem biomass and carbon sink capacity evaluation in China for future studies.
Here I have some comments:
- The dataset needs more clarifications. 1) in the “Description” page, there are two identical symbols named “n” with different meanings, and two identical symbols named “R2[R]” with different meanings. 2) in the “General” page, what do the “xx” and “mx” mean? 3) in the “Equation” page, symbol “H”, “C”, “Ac”, “D”, and “M” have not been defined. 4) the year of the raw data acquired should be mentioned for each equation. 5) the longitude and latitude, or the longitude and latitude range in which the raw data was collected and corresponding equation was generated, should be provided. The last two points are important, because the journal has one principle for the data description: “Specify the temporal and geographical scopes, and temporal and spatial resolutions of your data wherever appropriate”.
- The text to explain the dataset needs some revisions. 1) line 40, “Representative researches” should be “Representative researches in China”. 2) line 100, it is good if a schematic diagram could be presented to show the difference among the three types of shrub species. 3) the style of parameters in functions should be 4) line 148, the word “Consequently” can be replaced by “Collectively”. 5) Line 176, “The sample size varied from 5 and 312 shrubs”, this means in some samples data cannot be split into 10% and 90%, and the 90% part cannot be split into 75% and 25% for accuracy test. How many of them? And what’s the lowest limit of the sample size which can satisfy the statistical requirement of the recommended method? 6) line 183 and later, “valve” should be “value”.
Citation: https://doi.org/10.5194/essd-2021-44-CC1 -
AC1: 'Reply on CC1', Yang Wang, 17 Oct 2021
Thank you very much for your detailed suggestions. The specific revisions are as follows:
1、In the Description sheet, the first “n” is the number of shrub samples used in equation creation, we revised it to “n in equation creation”; the second “n” is the number of shrub samples used in equation evaluation, we revised it to “n in equation evaluation”.
The first “R2[R]” is the goodness-of-fit statistics used in equation creation, we revised it to “R2[R] in equation creation”; the second “R2[R]” is the equation evaluation statistics used in equation evaluation, we revised it to “n in equation evaluation”.
2、In the General sheet, we revised “xx” and “mx” to “inf” and “equ” which are short for “information” and “equation”, respectively.
3、In the Equation sheet, we clearly illustrated the meaning of symbols in “Remarks” column, include “H”, “C”, “Ac”, “Vc”, “D”, “D10”, “P”, “N”, “Ma” and “M”.
4、Field measured data of shrubs were obtained from 2011 to 2013, we added this information in the Introduction sheet instead of demonstrating it for each equation.
5、In the General sheet, we added the longitude and latitude, or the longitude and latitude range in which the raw data was collected and corresponding equation was generated.
6、We revised the text according to your suggestions in line 40, line148 and line183.
7、In some cases, equations with small sample sizes lack validation, they are all from the literature. The lowest limit of the sample size of each shrub species obtained from field measurement is 16.Citation: https://doi.org/10.5194/essd-2021-44-AC1
-
CC2: 'Comment on essd-2021-44', Xiangping Wang, 07 Aug 2021
This manuscript (MS) reports a large dataset for allometric equations to estimate shrub biomass, for a variety of species and sites across China. Interestingly, it seems that most equations were constructed by the present study based on sampling of 738 sites using a unified method. This greatly improved the quality and comparability of the equations, which avoid the weakness of compiling data from literatures (which were generally somehow different in sampling methods and thus led to uncertainty in data quality). Consequently, the dataset is clearly useful for improving the carbon pool/sink estimation of shrub ecosystems.
Considering the importance of this dataset, here I have some suggestions for the authors to improve the MS:
1) The Methods section needs to be clearer. As mentioned above, a major advantage of the dataset is that they have many equations based on their own measurements (the abstract said that they have 738 sites). However, the methods to obtain these equations were introduced together with the equations complied from literatures. This leads the readers not very clear about the details of the methods. For instance, did they obtain one or more equations for each of the 738 sites? How many equations from the 822 equations were measured by this study? In my opinion, similar methods issues are better introduced independent of the equations collected from the literatures.
2) As for the equations from the literatures, these are also good data in supplementary to the measured equations. However, the methods to compile, select and validate equations from literatures clearly are different, and may be better to be introduced in another section.
3) In the Excel file reporting the data, I suggest to add a column in the “Equation” sheet, which clearly indicate the data source of the equation (e.g. “this study” or “the reference”). Presently, this information is reported in the “General” sheet. This is not convenient for the readers (personally I would prefer the equations measured by the present study, as explained above). Meanwhile, I also suggest the ranges of shrub height, crown, diameter etc. to be given for each equation in the “Equation” sheet. These ranges are critical for readers to determine whether an equation can be used for their estimation. However, these ranges are now only given for each species, which is not convenient for the readers.
4) The abbreviations in the “Equation” sheet seemed not well described. For instance, what does Vc, Ma, Ac, N, etc. mean? I did not find these in the “Description” sheet. Did I miss something?
Citation: https://doi.org/10.5194/essd-2021-44-CC2 -
AC2: 'Reply on CC2', Yang Wang, 17 Oct 2021
Thank you very much for your revision suggestions.
1、In the “Materials and methods” section, methods of equation collection from the literatures were illustrated in “2.1 Literature retrieval” and “2.2 Equation collection and screening”. Methods to create equations with field measured data were illustrated in “2.3 Equation creation and evaluation”.
2、In the “Equation” sheet, we arranged information most directly related to equations, such as predictor variables, equation forms, equation coefficients and statistical parameters in equation evaluation, etc. Therefore, other information not most directly related to equations can be found in the “General” sheet through retrieval.
3、Description of abbreviations such as “H”, “C”, “Ac”, “D”, “M” in the “Equation” sheet were added in the “Description” sheet.Citation: https://doi.org/10.5194/essd-2021-44-AC2
-
AC2: 'Reply on CC2', Yang Wang, 17 Oct 2021
Data sets
A biomass equation dataset for common shrub species in China Yang Wang, Wenting Xu, Zhiyao Tang, Zongqiang Xie https://doi.org/10.11922/sciencedb.00641
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