Trait Gradient Analysis for Evergreen and Deciduous Species in a Subtropical Forest

Round 1

Reviewer 1 Report

The submitted paper is well written. I have two remarks.

First is related to the study area. It is written that: "area is part of the transitional zone between the subtropical and northern subtropical, tropical continental monsoon climate". In the lines 323-324 authors mentioned that geographical range is relatively small, however, the Xingdou Mountain, rises 1751.2 m above sea level. What about elevation gradient? Can it be a factor which has an influence on plant communities and trait variation?

Second: In lines 130-132 the authors presented seven traits taken into account to analysis. Please give 2-3 sentences on how each trait was count.

Author Response

Responses to comments of Reviewer 1

Question1:First is related to the study area. It is written that: "area is part of the transitional zone between the subtropical and northern subtropical, tropical continental monsoon climate". In the lines 323-324 authors mentioned that geographical range is relatively small, however, the Xingdou Mountain, rises 1751.2 m above sea level. What about elevation gradient? Can it be a factor which has an influence on plant communities and trait variation?

 

 Response1: Thank you for your concerns. We have clarified the descriptions on the basic geographical and climatic features of the studied sites, we add the geographical locations and ranges of the sample plots. The sentence "area is part of the transitional zone between the subtropical and northern subtropical, tropical continental monsoon climate" has some mistakes in expression, which should be changed into “The area is in the central subtropical to northern subtropical transitional zone, with a dominant continental monsoon climate".

Although the peak of Xingdou Mountain is high(1751.2 m) and the elevational span is large (360m-1751.2m), the elevation range of our sample plots  is between 900 to1200m, which, we think ,should not be a factor having significant influences on plant communities and trait variation. Please see Page5, Lines 109-110.

Question2 Second: In lines 130-132 the authors presented seven traits taken into account to analysis. Please give 2-3 sentences on how each trait was count.

Response 2:According to your suggestions, we add some sentences describing the seven traits, and adding how these seven traits were counted in the revision. See Page5, Line116-136

 

Revised paragraphs:

We established forty-eight permanent forest dynamics plots (0.04 ha) in Xingdoushan National Nature Reserve in Hubei Province of central China. The area is in the central subtropical to northern subtropical transitional zone, with a dominant continental monsoon climate. The elevational gradient of the Reserve ranges 360m to 1751.2 m. The elevational range of our forest dynamics plots is from 900m to 1200m, where typical old growth evergreen-deciduous broadleaved mixed forests distributed. For each plot, the diameter at breast height (DBH) and height (H) of all woody stems ≥ 1 cm DBH were recorded and identified to species level with the help of local botanists.

Specific leaf area, leaf area and leaf dry matter content are part of the leaf economic spectrum, collectively describing variation in light capture and carbon economy [5].Wood density is known to co-vary with other hydraulic traits and is thought to reflect variation in water acquisition and drought tolerance strategies [22, 23]. Leaf nitrogen and phosphorus content has a significant effect on the photosynthetic efficiency of plants [5], while the nitrogen-phosphorus ratio can serve as an indicator of which nutrient element is restricting plant community productivity. The most recent method determining plant functional traits [21] was adopted in our study [7]. Our study focused on seven functional traits ,including specific leaf area (SLA), leaf area (LA), leaf dry matter content (LDMC), wood density (WD), leaf nitrogen content (LNC), leaf phosphorus content (LPC) and nitrogen / phosphorus ratio (N:P).. The leaf area was first measured with a LI-COR3100C Area Meter (LI-COR,149 USA). Afterweighing mass of the fresh leaves, samples were then dried for 72 hours in a 60°C oven. We calculated SLA as the ratio of leaf area to leaf dry mass (cm² / g). We further quantified leaf nutrients (LNC and LPC) in the lab at Hainan University. To avoid negative effects on tree growth from the sampling process, we did not take tree trunk cores in forest stands. Instead, WD were calculated from measurements of 1-2 cm diameter branches. Bark was removed from the branches before measuring branch volume using an electronic balance. We dried branches in a 105°C oven for 72 hours and then recorded the dry weight. Nitrogen / phosphorus ratio was the ratio of leaf nitrogen content to leaf phosphorus content.

In each permanent forest dynamics plot, branches and leaves were collected from all individuals with DBH ≥ 10 cm. For individuals whose DBH was less than 10 cm, the five largest individuals of each species were sampled. If there were fewer than five individuals of a species in a sample plot, then all individuals of the species in the sample plot were sampled. In all, 2982 individuals were  sampledand measured  which represented all species present, including 99 deciduous species (1236 trees) and 72 evergreen species (1746 trees) in the 48 plots.

Reviewer 2 Report

In their manuscript ‘Trait gradient analysis for plants with contrasting leaf habits in a subtropical evergreen deciduous broad-leaf mixed forest of Central China’, the authors explore an interesting data set on vegetative trait variation between and among deciduous and evergreen species from subtropical forests, showing that intraspecific trait variation avoids competition, thus permitting coexistence in these megadiverse forests. This general conclusion of this study is interesting for an international readership, although a number of major and minor issues should be fixed prior to acceptance. I thus recommend ‘resubmission after major revision’.

Major concerns:

68. 53-68. The third paragraph of the introduction is confusing and requires major revision. Specifically, I cannot see how interspecific variation (l. 53) increases species adaptability. Then, ‘these differences .. among coexisting species’ (l. 56-57) is only partially true: Intraspecific differences in trait values emerge additionally in function of environmental differences among different stands; this should be considered throughout the ms. Finally, ‘interspecific trait variation indicates… ’ (l. 60): There is a vast amount of literature indicating that interspecific trait variation causes species turnover along environmental gradients, and I cannot see that intraspecific trait variation contributes to species turnover.

Throughout the ms:  the entire text requires a profound revision of English language style.

Methods (l.147-194): The explanation of alpha and beta components is confusing. The equations are not fully explained, the text between l. 165 and 180 does not fit to the figures and the figures do not facilitate the understanding of the two components. In the figure you should make clear that beta value is read from x-axis. Thus, it should be presented as a horizontal, not a vertical line (I cannot see were the beta value arrow in Fig. B points). Make clear, if alfa values were detected for species (Fig. B) or for individual trees (Fig. A). If B is true where do you measure alfa component, when the mean species value is not parallel to X=Y line? Why do you present mean values for evergreen and deciduous species, when they are not used for the computation of alfa or beta components? Show in this figure, that alfa components can achieve negative values, while beta components cannot – for that, it may be clearer to provide an additional figure containing normalized the trait data (in function o x-axis values), so that the x=y line becomes the x-axis (beta component) and you can read the alfa value directly from y axis. It is not necessary to show graphs for two different traits.

205. 195-205. Please details details about the pairwise correlation test – is it a correlation between evergreen and deciduous species? How was this done? This is essential to understand p-values in Fig. 2 and Table 2.

 

 

Minor concerns:

Title is too long and not really attractive (title indicates a case study only without interest for an international readership). Please try to capture the main result/conclusion and reduce to – at most – 15 words.

106. 105-106. ‘Alpha traits …’ This statement is oversimplified, see my comments on l. 53-56. Please revise and provide additional literature.
107. 116: Please specify uneven precipitation distribution.

l.121-123: Author and family names of species are lacking.

Table 1. Please add statistics to the values.

Figure 3-5. I suggest to change colors; there are people that cannot differentiate red and green. Perhaps parts of these figures may b

Author Response

Response to reviewer 2

Question1 In their manuscript ‘Trait gradient analysis for plants with contrasting leaf habits in a subtropical evergreen deciduous broad-leaf mixed forest of Central China’, the authors explore an interesting data set on vegetative trait variation between and among deciduous and evergreen species from subtropical forests, showing that intraspecific trait variation avoids competition, thus permitting coexistence in these megadiverse forests. This general conclusion of this study is interesting for an international readership, although a number of major and minor issues should be fixed prior to acceptance.

 

Response1:Thank you for your comments! We have done our best to follow your constructive suggestions on the improvement of our manuscript and have made revisions accordingly.

Major concerns:

Question 2: 53-68. The third paragraph of the introduction is confusing and requires major revision. Specifically, I cannot see how interspecific variation (l. 53) increases species adaptability. Then, ‘these differences .. among coexisting species’ (l. 56-57) is only partially true: Intraspecific differences in trait values emerge additionally in function of environmental differences among different stands; this should be considered throughout the ms. Finally, ‘interspecific trait variation indicates… ’ (l. 60): There is a vast amount of literature indicating that interspecific trait variation causes species turnover along environmental gradients, and I cannot see that intraspecific trait variation contributes to species turnover.

 

Response 2 Yes, your statements are true. We have rewritten the paragraph according to your opinions.

The revised paragraph is as follows:

Trait variation analyses have been dominated by examinations on interspecific trait variations in most studies ,which involve systems with  pronounced environmental gradients [13]. Trait differences among species primarily explain the greater extent and denser filling of community trait space at increased plant diversity. The trait plasticities among species in response to environmental gradients did not increase community-wide trait space or decrease niche overlap between species [14]. However, intraspecific differences in trait values emerge additionally in function of environmental differences among different stands .There is a growing body of evidence showing that the role of intraspecific trait variability is increasingly recognized as a key factor shaping plant fitness and community assembly worldwide [15]. Intraspecific trait variation-due to phenotypic plasticity or local adaptation-has a significant and non‐negligible effect on species properties and ecosystem function. For instance, accounting for intraspecific trait variation helps to better understand phenotypic plasticity, community assembly and ecosystem processes in community ecology [16]. At the community level, it is more complex to disentangle the relative magnitude of intra- and interspecific trait variation in community assembly [17]. Habitat filtering indeed reduces both intra- and interspecific trait variation through adaptation to abiotic constraints [11], while niche differentiation can reduce intraspecific trait variation (reduction of niche width through increasing species packing) and increase interspecific trait variation. The limit of coexistence (limiting similarity) of species depends on the ratio between interspecific niche differences (defined by interspecific trait variation) and species’ niche width (i.e. intraspecific trait variation) [17]. Therefore, quantifying the relative extent of intra- and interspecific variability in communities may help us in understanding species coexistence and maintenance of species diversity.

13. Sébastien, Auger; Shipley, B. Inter-specific and intra-specific trait variation along short environmental gradients in an old-growth temperate forest. Journal of Vegetation Science.2013, 24(3):419-428. https://10.2307/23466935
14. Roscher, C.; Schumacher, J.; Marlén, Gubsch; et al. Interspecific trait differences rather than intraspecific trait variation increase the extent and filling of community trait space with increasing plant diversity in experimental grasslands. Perspectives in Plant Ecology Evolution & Systematics. 2018. https://10.1016/j.ppees.2018.05.001
15. de, Smedt, P.; Ottaviani, G.; Wardell-Johnson, G.; et al. Habitat heterogeneity promotes intraspecific trait variability of shrub species in Australian granite inselbergs. Folia Geobotanica. 2018. https://10.1007/s12224-018-9311-x
16. Midolo, G.; Frenne, P. D.; Norbert, Hölzel; et al. Global patterns of intraspecific leaf trait responses to elevation. Global Change Biology. 2019, 25(7). https://10.1111/gcb.14646。
17. Albert, C.H.; Grassein, F.; Schurr, F. M.;. When and how should intraspecific variability be considered in trait-based plant ecology? Perspectives in Plant Ecology Evolution and Systematics. 2011, 13(3):0-225. https://10.1016/j.ppees.2011.04.003

 

Question3 Throughout the ms:  the entire text requires a profound revision of English language style.

Response3: We would like to have our manuscript be proofread by the English language proofreading services assigned by Forests to meet the language requirement of the journal.

 

Question4 Methods (l.147-194): The explanation of alpha and beta components is confusing. The equations are not fully explained, the text between l. 165 and 180 does not fit to the figures and the figures do not facilitate the understanding of the two components. In the figure you should make clear that beta value is read from x-axis. Thus, it should be presented as a horizontal, not a vertical line (I cannot see were the beta value arrow in Fig. B points). Make clear, if alfa values were detected for species (Fig. B) or for individual trees (Fig. A). If B is true where do you measure alfa component, when the mean species value is not parallel to X=Y line? Why do you present mean values for evergreen and deciduous species, when they are not used for the computation of alfa or beta components? Show in this figure, that alfa components can achieve negative values, while beta components cannot – for that, it may be clearer to provide an additional figure containing normalized the trait data (in function o x-axis values), so that the x=y line becomes the x-axis (beta component) and you can read the alfa value directly from y axis. It is not necessary to show graphs for two different traits.

Response 4 :Thanks for your detailed reading and patient guidance.

On the interpretation of TGA methods and the graph of α and β, we mainly refered to articles of Ackerly and Cornwell(2007) and Kooyman et al. (2010). We made our figures according to the above references,and carefully examined the method and provided the legend in this new revision. In our paper,d we use the two functional traits, SLA and WD to represent plant functional strategies.

The following two figures are listed for your reference:

Figure 1  Scatterplot of species trait values (tij) vs. abundance weighted plot mean trait values (pj) for log10 SLA (cm²/g) in 44 woody plant communities of Jasper Ridge Biological Preserve. [From Ackerly and Cornwell (2007)]

Fig. 2. Scatterplot of species trait values versus abundance-weighted plot-mean trait values for log₁₀ leaf area cm^-2 in 216 woody plant assemblages (representing two main rain forest communities in north-east New South Wales). [From Kooyman et al. (2010)]

 

Question5 195-205. Please details details about the pairwise correlation test – is it a correlation between evergreen and deciduous species? How was this done? This is essential to understand p-values in Fig. 2 and Table 2.

Response5: We have detailed the method of pair correlation test in the method section of this new revision. The purpose of this paper is to compare the difference between evergreen species and deciduous species in functional features. Therefore, the pairwise correlation is a correlation between evergreen and deciduous species, which has been done by correlating the trait matrix of the evergreen species to that of the deciduous one in each examined trait. The test was performed by “corrplot” in R. 

 

Question6 Minor concerns:

Title is too long and not really attractive (title indicates a case study only without interest for an international readership). Please try to capture the main result/conclusion and reduce to – at most – 15 words.

--We changes the title as “Trait gradient analysis for evergreen and deciduous species in a subtropical forest”

105-106. ‘Alpha traits …’ This statement is oversimplified, see my comments on l. 53-56. Please revise and provide additional literature.

--Done. Please see P4, L87-88.

116: Please specify uneven precipitation distribution.

--Done. Please see P4, L97-98.

l.121-123: Author and family names of species are lacking.

--Done. We added the family name and author of species. Please see P4, L102-106.

Table 1. Please add statistics to the values.

---Done，Please see Table 1.

Figure 3-5. I suggest to change colors; there are people that cannot differentiate red and green. Perhaps parts of these figures may b

---Done，Please see Figure 3-5.

Round 2

Reviewer 2 Report

Although the author made a good job to review their manuscript, there are some major issues that remain unanswered and require further improvements.

 

First, Fig. 1 and 2 were not reviewed according my comments. 

Fig. 1 - what are different symbols and lines? Authors should use different line types for different species and explain together with symbols in a legend. What are open symbols? SPecies mean values? What is Ri, bi, alpha-i and beta-i (should be explained in legend)? Again, alpha-i is the distance between regression line of species i (continuous line) to mean trait values in community (dashed line), so it is a distance and may be represented by a vertical line/arrow as it is. It is unclear if beta-i is the value of the x-axis of the species mean value, e.g., empty quadrat of the Fig (then it should be represented by the same arrow type as alpha-i with the term beta-i cetnralized to this arrow), or is it the x-value of the mean species value (then, it should be represented by a dashed line and an anotation below the y-axis, not above).

Fig. 2 - basically, the same issues apply on fig. 2. Additionally, I wonder why the three example species show perfect vertical lines - did you plot mean species trait values on y axis? 

My second concern is regarding the pair correlation test. You have only seven functional traits, so if you correlate mean trait values of evergreen and seasonal species you have a problem with overfitting that should be addressed. On the other hand, you state that you use the trait matrices for correlation, but information regarding the correlation of matrices (including different number of species) is lacking. The corrplot function given in the text allows only the graphic representation of a correlation matrix.  

Author Response

Dear editor and reviewers

Thank you for your constructive comments and helpful suggestions on our manuscript-forests-727399! We have made our best efforts in revising the manuscript according to your concerns. Apart from the academic requirements of the journal, if a language improvement is needed, we would like to have our manuscript be proofread by the English language proofreading services assigned by Forests to meet the language requirement of the journal. We appreciate your consideration for publication of our manuscript in Forests. The following are our responses to the comments of the referees.

With best regards,

Runguo Zang on behalf of all the coauthors.

 

 

Responses to comments of reviewer 2

First, Fig. 1 and 2 were not reviewed according my comments. 

Fig. 1 - what are different symbols and lines? Authors should use different line types for different species and explain together with symbols in a legend. What are open symbols? SPecies mean values? What is Ri, bi, alpha-i and beta-i (should be explained in legend)? Again, alpha-i is the distance between regression line of species i (continuous line) to mean trait values in community (dashed line), so it is a distance and may be represented by a vertical line/arrow as it is. It is unclear if beta-i is the value of the x-axis of the species mean value, e.g., empty quadrat of the Fig (then it should be represented by the same arrow type as alpha-i with the term beta-i cetnralized to this arrow), or is it the x-value of the mean species value (then, it should be represented by a dashed line and an anotation below the y-axis, not above). Fig. 2 - basically, the same issues apply on fig. 2. Additionally, I wonder why the three example species show perfect vertical lines - did you plot mean species trait values on y axis? 

 

Response1: Thank you for your concerns.

We reinterpreted and annotated Fig. 1. We listened to your suggestion, chose SLA as the case of display, and deleted the case WD in Figure 1. Then we have selected the dominant tree species to highlight scatterplot of species trait values vs. abundance-weighted plot mean trait values. Finally, we explain the meaning of the symbols and lines in the legend, and also, given an example of the meaning of the formula, is t_i=alpha + beta. According to the perfect vertical lines, that’s from top to bottom were the least squares regression of deciduous, all and evergreen species. And red lines represent the niche breadth, beta values and ti. And we gave the modified version in line 198-211.

We also given legend explanation and remarks to figure 2 in line 241-247

In Fig. 2, the abscissa was alpha values or beta values, and the ordinate was the frequency of alpha or beta values. Purple represents evergreen species, gray represents deciduous species, light color represents evergreen and deciduous species overlap. Red lines represent median of α and β values. So, we had re-corrected the figure.

 

My second concern is regarding the pair correlation test. You have only seven functional traits, so if you correlate mean trait values of evergreen and seasonal species you have a problem with overfitting that should be addressed. On the other hand, you state that you use the trait matrices for correlation, but information regarding the correlation of matrices (including different number of species) is lacking. The corrplot function given in the text allows only the graphic representation of a correlation matrix.

Response3: Thank you for your concerns.

Overfitting is mainly due to the use of overly complex models, data noise, and limited data sets. For reducing overfitting, we collected all trees with DBH ≥10 cm in the plot. In all, 2982 individuals were sampled and measured  which represented all species present, including 99 deciduous species (1236 trees) and 72 evergreen species (1746 trees) in the 48 plots. And we provided information on the number of different species in 2.2. Data Collection and Analysis, line 158-163. And “The test was performed by “corrplot” in R” was wrong in line 225, so we deleted the sentence.

 

 

Author Response File: Author Response.docx