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Article

Effect of Gap Sizes on Specific Leaf Area and Chlorophyll Contents at the Castanopsis kawakamii Natural Reserve Forest, China

by
Supaporn Buajan
1,2,
Jinfu Liu
1,*,
Zhongsheng He
1 and
Xueping Feng
1
1
College of Forestry, Fujian Agriculture and Forestry University, No.15, Shang-xia-dian Road, Cangshan District, Fuzhou 350002, Fujian, China
2
Tree-Ring and Climate Change Research Center, Faculty of Environment and Resource Studies, Mahidol University, 999 Phutthamonthon 4 Road, Salaya, Phutthamonthon, Nakhon Pathom 73170, Thailand
*
Author to whom correspondence should be addressed.
Forests 2018, 9(11), 682; https://doi.org/10.3390/f9110682
Submission received: 21 September 2018 / Revised: 20 October 2018 / Accepted: 26 October 2018 / Published: 30 October 2018
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
The two hemispherical photographs (THP) method was used to calculate gap area. The areas of nine forest gaps were measured. Meanwhile, non-gap areas were selected as control groups with areas of 225 m2. Plots with areas of 25 m2 in five different directions within gap and non-gap areas were conducted for collecting leaf samples. To determine the effect of gap size on leaf traits the selected traits were leaf area (LA), leaf dry mass (LDM), specific leaf area (SLA), Chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and carotenoid (CAR). Leaves were collected from the regeneration layer (<2 cm DBH, height 2–5 m) to measure the leaf traits in winter and summer seasons. Results confirmed significant positive correlations between LA and LDM in the small, medium, large gap sizes, and non-gap areas (r2 = 0.913, 0.827, 0.897, and 0.939, p < 0.01, respectively). On the contrary, relationships between LDM and SLA in the small, medium, large gap sizes, and non-gap areas have significant negative correlations (r2 = −0.269, −0.259, −0.417, and −0.505, p < 0.05, respectively). The effect of gap size on the average Chl a, Chl b, TChl, and CAR varies by the season. During the summer season, the highest chlorophyll contents were recorded in the small gap size and the lowest in the non-gap area, while during the winter season, the highest values of these chlorophyll contents appeared in the medium gap size. Moreover, the directions within the gap in the medium gap size of the summer season had an effect on the Chl a and TChl.

1. Introduction

Forest gaps are the areas in the forest created when trees are struck by lightning, blown over by storms, knocked down by other falling trees, or die of old age. When a forest gap appears it alters the environment for all the surrounding plants in the open area. Forest gaps maintain species regeneration and species diversity [1,2]. Gaps create specific climates within the gap area. A previous study about the gap size and microclimate found that the relationship between gap size and microclimate is complicated. The climate within a gap does not depend on the gap size [3]. Location and timing of the gap created are the main factors that influence the microclimate and regeneration dynamics. The changing environment also has effect on species regeneration in that area. Each plant species responds to these factors in a different way depending on the functional traits of plant.
Plant functional traits are the feature of each individual plant species (physical, chemical, morphological, life history, etc.) that respond to environmental physical fitness. Each individual species interacts with the environment in a different way [4]. Many ecologists use plant functional traits to study the ecosystem such as biodiversity, plant structure and community, environmental condition, ecological processes, competitive determinant, population stability, etc. [5,6,7,8]. Plant responses to environmental factors vary by species and traits. Soliveres et al. (2014) [9] studied the plant–plant interaction at the global scale. They found that the interaction between plants within a woody ecosystem is more sensitive to environmental changes than in grassland ecosystems, indicating that the region is the determining factor driving plant–plant interaction. Plant functional traits are highly dependent on the environmental conditions, and it is a good factor to use to determine the interaction between the plant and its environment [10]. Studies about plant trait responses to the environment and effects on ecosystem properties show that different ecosystems produced opposing plant traits such as specific leaf area and leaf nutrient with effects on litter decomposition [11]. Berner and Law (2016) used data sets of plant traits for 35 tree and shrub species across Oregon and Northern California between 1999 and 2014 to evaluate the model of plant traits responding to environmental conditions [12]. Many studies on plant performance in forest gaps found that microenvironmental conditions within the gap had an effect on plant performance. Zhu et al. (2014) [13] further studied the effect of forest gap on the regeneration of woody plants and found that the regulation of woody plants responding to the environment in the gap depends on the forest type, gap characteristics, and environmental factors.
There are many plant functional traits that ecologists can choose from that are suitable for their research purpose. Each plant traits responds to a different environmental factor. Light is the most altered factor when a gap is created and the leaf is the dominant part of the plant that is affected by light [14]. Leaf traits are an indicator of plant performance [15]. Many research studies focus on leaf traits because they are not only used to determine photosynthesis influenced by a light factor [16,17,18] but it is also related to soil factors [19,20,21]. Specific leaf area has been proved to be the best trait that relates to whole plant growth [22,23]. Specific leaf area explains, to a greater extent, the variation in growth between species. It is noted that under optimum conditions species with higher growth will have greater specific leaf area. A decrease in specific leaf area indicates that the leaf has higher biomass per unit leaf area [24]. In the photosynthesis process, the photoreceptors absorbing radiation energy are the chlorophylls [25] and chlorophyll content is related to the plant growth [26]. Moreover, specific leaf area is the easiest parameter to measure and is related to other leaf traits such as nitrogen and phosphorous content [27,28] and relative growth rate [29,30]. Thus, in this study, leaf traits such as leaf area (LA), leaf dry mass (LDM), specific leaf area (SLA), chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and carotenoid (CAR) were selected. The aim of this study is to investigate the effect of gap size on selected leaf traits. The result of this study will help in better understanding how the size of the forest gap influences leaf traits. The gap size has different effects on leaf traits. Each leaf trait in this study varied by species and season.

2. Materials and Methods

2.1. Study Site and Gap Area Determination

The study site, located at 26°07′–26°12′ N and 117°24′–117°29′ E, is the Green Hook Castanopsis (Castanopsis kawakamii) Natural Reserve Forest in Sanming City, Fujian Province, China (Figure 1a). The climate in this region is a middle subtropical monsoon. Annual rainfall is 1500 mm of which 75% of rainfall occurs between March and August. The mean annual temperature is about 19.5 °C and the daily mean minimum is −5.5 °C, maximum is 40 °C. Annual average relative humidity is 79%, and the mean wind velocity is 1.6 m/s (average of 40 years data collected from the Sanming Climatological Bureau, China). Soil types under are mainly dark red earth type and red earth; purple soil is the second most dominant with 1 m of soil thickness [31].
The two hemispherical photographs (THP) method was used to calculate the gap area by using a fish-eye lens camera. Two photos at different heights (low photo and high photo) of each gap were taken. The Adobe Illustrator CC 2014 computer software program (Adobe Systems Computer Software Company, San Jose, CA, USA) was used to draw the line in the photo. Eighteen lines were drawn at 10° intervals through the image center of each gap. The length of each line was measured from the center of the image to the canopy edge (in pixels). The unit of length in pixels converted to the centimeter 1 cm = 1 pixels/118.11 (this ratio depends on the resolution of the photo) [32]. The images were taken in June 2014. The areas of nine forest gaps were calculated. The largest area is 216.72 m2 and the smallest area is 30.28 m2. According to gap area size the nine forest gaps were classified into three small (30–50 m2), three medium (50–100 m2), and three large gaps (>150 m2). The small gap size was created by branch fall, while the large and medium gap sizes were created by tree fall. Meanwhile, three non-gap areas, which were canopy covered, were selected as control groups with areas of 225 m2.

2.2. Leave Collection and Analysis

In each gap and non-gap area, multiple leaf samples were collected from 5 × 5 m sampling plots at five positions (center, north, south, east, and west as shown in Figure 1b). Young but fully expanded and hardened leaves from species regeneration layer were collected (<2 cm DBH, height = 2–5 m). Leaves with obvious symptoms of a pathogen or herbivore attack were avoided. Samples were taken from the part of the plant lit by direct sunlight at the time of sampling [33]. Each data point was obtained from the measurement of 10 replicate samples per species. The samples were scanned by scanner then the area of each leaf (LA) was measured with digital micrographs using image analysis software. The LA was expressed in mm2, then the leaf was dried in an oven at 70 °C for at least 48 h [34]. After that, the mass of dried leaves (LDM) was measured and expressed in mg. SLA was measured by Equation (1) [34]. The LA, SLA, and LDM were investigated only in the summer season (August 2014).
SLA = LA (mm2)/LDM (mg)
For chlorophyll content analysis, the leaf sample was cleaned with tissue paper and then cut into small pieces. The chlorophyll content of the 0.1 mg samples was obtained by extracting in 5 mL 80% acetone solution mixed with 5 mL 95% ethanol. The samples were placed in the dark for 24 h. Based on Mackinney’s work, we measured extract absorbance using a Campspec M 501 Single Beam UV/vis Spectrophotometer at λ = 470, 663, and 645 nm. Chlorophyll concentrations were calculated using Arnon’s formulas [35] as shown in Equations (2)–(5); the unit of chlorophyll concentration is milligram per gram (mg/g).
Chl a = (12.7 × A663 − 2.69 × A645) × V/W/1000
Chl b = (20.13 ×A645 − 5.103 × A663) × V/W/1000
CAR = (4.4 × A470 − 0.01 × Chla − 0.45 × Chlb) × V/W/1000
TChl = Chl a + Chl b
where Aλ is the absorbance reading at wavelength λ (nm), V is the total volume of sample extracted (mL), and W is the Fresh weight of leaf (g). The leaf samples analyzed for chlorophyll content were collected in different leaf cohorts with one set produced in the summer (August 2014) and the others produced in the winter (January 2015).

2.3. Statistical Analysis

The average, maximum, and minimum values of LA, LDM, SLA, Chla, Chlb, TChl, and CAR were calculated using Microsoft Excel 2007. The correlations were calculated using the Pearson correlation method. The relationship among leaf traits was estimated by linear regression. The variation of the leaf traits among and between three groups of gap sizes and gap directions were conducted by one-way ANOVA as well as multiple comparisons by the Tukey post-hoc test method, (p < 0.05). All data were analyzed using the program SPSS 16.0 (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Gap Sizes on LA, LDM, and SLA

A total of 50 species regeneration layer were collected for leaf traits analysis. Results confirmed significant positive correlations between LA and LDM in the small, medium, and large gap sizes as well as the non-gap areas (r2 = 0.913, 0.827, 0.897, and 0.939, p < 0.01, respectively). On the contrary, relationships between LDM and SLA in the small, medium, and large gap sizes as well as non-gap areas had significant negative correlations (r2 = −0.269, −0.259, −0.417, and −0.505, p < 0.05, respectively) (Figure 2). Interestingly, only the SLA and LA in the non-gap area had a significant negative correlation (r2 = −0.407, p < 0.05). The mean values of SLA displayed significant differences between directions. The SLA at the center of the gap was lower when it was compared to the north direction (p = 0.037). Moreover, there was no significant difference with other directions (Figure 3).
Results showed that the average values of LA for four species had significant differences among gap sizes. The averaged values of LA for Litsea subcoriacea Yen C. Yang & P. H. Huang, Schima superba Gardner & Champ, Itea omeiensis C.K. Schneider, and green hook castanopsis (Castanopsis kawakamii) had significant differences with gap sizes (p = 0.018, 0.034, 0.023, and 0.015, respectively). The multiple comparison analysis by the Tukey’s post-hoc test (p < 0.05) of LA in these four species is shown in Figure 4. There was no significant difference of LDM and SLA among gap sizes. Due to the limitation of the number of samples, other species were not suitable for ANOVA analysis.

3.2. Gap Sizes on Leaf Chlorophyll Contents

Relationships among Chl a, Chl b, TChl, and CAR in summer and winter seasons were analyzed by Pearson correlation. The results are shown in Table 1.
Gap sizes had an effect on the chlorophyll in the summer season. Chl a, Chl b, TChl, and CAR had statistically significant differences among gap sizes and non-gap areas as calculated by ANOVA (F (3253) = 9.407, 12.305, 15.217, and 8.534, p = 0.000, 0.000, 0.000, and 0.000, respectively). The multiple comparison analysis by the Tukey’s post-hoc test (p < 0.05) of Chl a, Chl b, TChl, and CAR in the summer season is shown in Figure 5.
Chl a, Chl b, TChl, and CAR in the winter season had statistically significant differences among gap sizes as calculated by ANOVA (F (2230) = 3.807, 4.249, 3.973, and 3.766, p = 0.024, 0.015, 0.020, and 0.025, respectively). The multiple comparison analysis of Chl a, Chl b, TChl, and CAR is shown in Figure 6.
Moreover, results showed that the Chl a and TChl in the summer season had significant differences among the five directions within gaps (F (4228) = 3.577 and 3.100, p = 0.007 and 0.016, respectively), while no significant differences were observed for Chl b and CAR. The multiple comparison analysis of Chl a and TChl in the summer season is shown in Figure 7. In the winter season, there was also no significant difference found among directions.
Analysis of the effects of gap size on the chlorophyll content in each species showed that only three species were influenced by gap size in this study. The Chl a, Chl b, and TChl contents in the summer season of L. subcoriacea had significant differences among gap sizes (p = 0.025, 0.017, and 0.008, respectively). The CAR content in the summer season as well as Chl a and CAR content in the winter season of S. superba had significant differences among gap sizes (p = 0.003, 0.036, and 0.035, respectively). The Chl a, Chl b, and TChl contents in the summer season as well as Chl a and TChl contents in the winter season of Symplocos lancifolia Siebold & Zucc had significant differences among gap sizes (p = 0.001, 0.008, 0.001, 0.003, and 0.017, respectively). The multiple comparison analysis of chlorophyll content of these three species in different gap sizes is shown in Figure 8.

4. Discussion

4.1. Effect of Gap Size on LA, LDM, and SLA

The positive correlations between LA and LDM in every gap size and non-gap area were consistent with the fact that large leaves usually had higher mass than small leaves [36,37]. The simple formula of the relationship between LA and LDM (Laβ = LDMα, where β is a proportionality constant and α is a scaling exponent) indicated that LA increased when LDM increased [38,39]. The result of the negative correlation between SLA and LDM in this study was consistent with the studies of SLA in turkey oak (Quercus cerris L.) [40]. Typically, an increase in SLA was associated with a decrease in LDM due to the fact that SLA was calculated from the ratio of LA to LDM. The negative correlation of SLA and LA, which was found in the non-gap areas in this study, was supported by the other studies of leaf traits and climate. Note that SLA may decrease with an increase in LA following the temperature gradient [41,42]. The directions within the gap had effects on the average values of SLA. The results of this study showed that the average values of SLA were lowest at the center of the gap. The low values of SLA were related to two factors, one of which is light intensity [43]. Buajan et al. (2017) reported that light intensity had an effect on the microenvironment in different gap sizes and SLA in each direction within the gap when compared with non-gap areas [44]; this was also observed for the amount of soil nutrients [45,46]. Leaf traits, including SLA, were increased with the increasing of soil nutrients [20]. Because the soil nutrients in this study were not significantly different among directions within the gaps the critical factor might be light intensity. Generally, light intensity has an effect on leaf development [47]. Reports relating the effect of light on invasive plant species showed that plant responded to low light by increasing biomass and forming large leaves leading to a high value of SLA. This is contrary to plants in high light intensity [48]. The study of SLA in white meadowsweet (Spiraea alba Du Roi) and steeplebush (Spiraea tomentosa L.) reported that the SLA of both species decreased with an increase of light intensity [49]. In this study, low values of SLA for four species (L. subcoriacea, S.a superba, I. omeiensis, and C. kawakamii) were found in the large gap size, while the high values of SLA appeared in the small gap size. According to the effect of light on SLA, the large gap size received higher light intensity compared to the small gap size as previously confirmed by several studies [44,50,51].

4.2. Effect of Gap Size on Chlorophyll Contents

Chl a, Chl b, TChl, and CAR had positive correlations with each other according to the mechanical production of these pigments in leaf. The relationship between TChl and CAR in this study was similar to the study of chlorophyll content in robusta coffee (Coffea canephora Pierre) leaves, which were found to have a strong correlation between TChl and CAR [52]. This positive correlation was also found in the maple, chestnut, wild vine, and beech leaves [53].
Chl a, Chl b, and CAR are pigments in leaves that are used for the photosynthetic process. The photosynthesis potential is determined by chlorophyll content [54]. The different values of chlorophyll content of three species (L. subcoriacea, S. superba, and S. lancifolia) in this study were confirmed by other studies that found the variations of chlorophyll contents within different species [55,56]. Environmental factors, especially light [57], also had an effect on the chlorophyll content, confirmed by significant differences between gap sizes, which were similar to the other results of the chlorophyll content of these three species. In summary, the main factor that affects the chlorophyll contents of L. subcoriacea is light intensity. The highest values of chlorophyll content of L. subcoriacea was found in small gap size because most of the samples were located at the center of small gaps, while they were not located at the center for medium and large gaps. However, it is not only light intensity but also soil conditions that have an effect on the chlorophyll contents [58,59], relating to the difference of chlorophyll contents of S. superba and S. lancifolia in different gap sizes which were influenced by soil conditions. The seasonal variation in chlorophyll also depends on species [60]. The effect of gap sizes on the average values of Chl a, Chl b, TChl, and CAR varies by season. During the summer season, the highest values of these chlorophyll contents appeared in the small gap size and the lowest values appeared in the non-gap areas, while the highest values of these chlorophyll contents appeared in the medium gap size during the winter season. This result indicated that gap sizes in the different seasons did affect chlorophyll content in different ways. This might be due to the seasonal changes of microenvironmental conditions within the gaps. Houter and Pons (2005) reported that photoinhibition increased with an increase in gap size, which was related to increased light intensity [61]. Moreover, the directions within the gap in the medium gap size of the summer season had an effect on the Chl a and TChl.

5. Conclusions

The relationship between LA and LDM in the small, medium, and large gap sizes as well as non-gap areas showed significant positive correlations. Conversely, relationships between LDM and SLA in the small, medium, and large gap sizes as well as non-gap areas had significant negative correlations. Moreover, the SLA at the center of the gap was lower when it was compared to the north direction. The effect of gap size on average Chl a, Chl b, TChl, and CAR contents varies by season. During the summer season, we recorded the highest chlorophyll contents in the small gap size and the lowest in the non-gap area, while during the winter season, the highest values of these chlorophyll contents appeared in the medium gap size. Moreover, the directions within the gap in the medium gap size during the summer season had an effect on Chl a and TChl. Gap sizes from this study had different effects on the leaf traits. Each leaf trait varied by species, age, seasonal, environmental factors, etc.

Author Contributions

Conceptualization, Z.H.; Data curation, S.B. and X.F.; Formal analysis, S.B.; Funding acquisition, J.L.; Investigation, S.B.; Supervision, J.L.; Writing—original draft, S.B.; Writing—review & editing, S.B.

Funding

This research was funded by the National Natural Science Foundation of China (NSFC), grant number 31700550 and 31770678. The APC was funded by the Fujian Agriculture and Forestry University.

Acknowledgments

We wish to express our thanks for the support received from the Castanopsis kawakamii Nature Reserve in Sanming City, Fujian Province to allow us to collect samples. We are also deeply thankful to Thomas Neal Stewart for editing and proofing the English language of this manuscript. We also thank the anonymous referees for their valuable input and criticism regarding the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) Location of the study site at Sanming City (red triangle), (b) plots size was 5 × 5 m (solid line) in five directions of gap area (dashed line) for leaf sample measuring. C, S, E, N, and W indicate center, south, east, north, and west, respectively.
Figure 1. (a) Location of the study site at Sanming City (red triangle), (b) plots size was 5 × 5 m (solid line) in five directions of gap area (dashed line) for leaf sample measuring. C, S, E, N, and W indicate center, south, east, north, and west, respectively.
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Figure 2. Correlations among leaf area (LA) (cm2), leaf dry mass (LDM) (mg), and specific leaf area (SLA) (m2/kg) in (a) small gap; (b) medium gap; (c) large gap sizes; and (d) non-gap areas. **, Correlations is significant at the 0.01 level. *, Correlations is significant at the 0.05 level. LA: leaf area. LDM: leaf dry mass. SLA: specific leaf area.
Figure 2. Correlations among leaf area (LA) (cm2), leaf dry mass (LDM) (mg), and specific leaf area (SLA) (m2/kg) in (a) small gap; (b) medium gap; (c) large gap sizes; and (d) non-gap areas. **, Correlations is significant at the 0.01 level. *, Correlations is significant at the 0.05 level. LA: leaf area. LDM: leaf dry mass. SLA: specific leaf area.
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Figure 3. Bar graph and statistical analysis of average SLA (m2/kg) in each direction (ANOVA, Tukey’s post-hoc test, p < 0.05). Error bars show the standard deviation values. Different letters over error bars indicate statistically significant results.
Figure 3. Bar graph and statistical analysis of average SLA (m2/kg) in each direction (ANOVA, Tukey’s post-hoc test, p < 0.05). Error bars show the standard deviation values. Different letters over error bars indicate statistically significant results.
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Figure 4. Bar graph and statistical analysis of LA (cm2) of Litsea subcoriacea, Schima superba, Itea omeiensis, and green hook castanopsis (Castanopsis kawakamii) in each gap size (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value; Different letters over bars indicate significant results.
Figure 4. Bar graph and statistical analysis of LA (cm2) of Litsea subcoriacea, Schima superba, Itea omeiensis, and green hook castanopsis (Castanopsis kawakamii) in each gap size (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value; Different letters over bars indicate significant results.
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Figure 5. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR in the summer season for each gap size and non-gap area (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
Figure 5. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR in the summer season for each gap size and non-gap area (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
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Figure 6. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR in the winter season for each gap size (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
Figure 6. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR in the winter season for each gap size (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
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Figure 7. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR in the summer season for each direction (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
Figure 7. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR in the summer season for each direction (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
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Figure 8. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR of Litsea subcoriacea (a), Schima superba (b), and Symplocos lancifolia Siebold & Zucc (c) in each gap size (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
Figure 8. Bar graph and statistical analysis of Chl a, Chl b, TChl, and CAR of Litsea subcoriacea (a), Schima superba (b), and Symplocos lancifolia Siebold & Zucc (c) in each gap size (ANOVA, Tukey’s post-hoc test, p < 0.05); error bars show the standard deviation value. Different letters over error bars indicate statistically significant results.
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Table
Table 1. Correlation coefficients (r) for the relationships among Chl a, Chl b, TChl, and CAR for gap sizes and non-gap area during summer and winter seasons.
Table 1. Correlation coefficients (r) for the relationships among Chl a, Chl b, TChl, and CAR for gap sizes and non-gap area during summer and winter seasons.
Chlorophyll Content (mg/g)
SummerWinter
Chl aChl bTChlCARChl aChlbTChlCAR
Small gap sizeSummerChl a 0.328 **0.927 **0.883 **0.392 **0.311 **0.369 **0.283 *Medium gap size
Chl b0.916 ** 0.594 **0.459 **nsnsnsns
TChl0.962 **0.991 ** 0.862 **0.404 **0.335 **0.386 **0.320 **
CAR0.300 **nsns 0.384 **0.304 **0.362 **0.348 **
WinterChl ansnsns0.378 ** 0.917 **0.989 **0.896 **
Chl bnsnsns0.285 *0.896 ** 0.966 **0.926 **
TChlnsnsns0.351 **0.985 **0.960 ** 0.925 **
CARnsnsns0.271 *0.898 **0.931 **0.934 **
Large gap sizeSummerChl a 0.902 **0.988 **0.922 **0.634 **0.610 **0.631 **0.593 **Non-gap area
Chl b0.801 ** 0.958 **0.749 **0.545 **0.502 *0.534 *0.464 *
TChl0.914 **0.975 ** 0.881 **0.618 **0.587 **0.612 **0.562 **
CAR0.333 **nsns 0.690 **0.712 **0.703 **0.718 **
WinterChl ans−0.315 **−0.227 *0.495 ** 0.972 **0.997 **0.954 **
Chl bns−0.345 **−0.266 *0.498 **0.955 ** 0.988 **0.980 **
TChlns−0.328 **−0.243 *0.501 **0.995 **0.980 ** 0.969 **
CARns−0.334 **−0.255 *0.489 **0.966 **0.964 **0.976 **
Chl a = Chlorophyll a; Chl b = Chlorophyll b; TChl = Total Chlorophyll; CAR = Carotenoid, * p < 0.05, ** p < 0.01.

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MDPI and ACS Style

Buajan, S.; Liu, J.; He, Z.; Feng, X. Effect of Gap Sizes on Specific Leaf Area and Chlorophyll Contents at the Castanopsis kawakamii Natural Reserve Forest, China. Forests 2018, 9, 682. https://doi.org/10.3390/f9110682

AMA Style

Buajan S, Liu J, He Z, Feng X. Effect of Gap Sizes on Specific Leaf Area and Chlorophyll Contents at the Castanopsis kawakamii Natural Reserve Forest, China. Forests. 2018; 9(11):682. https://doi.org/10.3390/f9110682

Chicago/Turabian Style

Buajan, Supaporn, Jinfu Liu, Zhongsheng He, and Xueping Feng. 2018. "Effect of Gap Sizes on Specific Leaf Area and Chlorophyll Contents at the Castanopsis kawakamii Natural Reserve Forest, China" Forests 9, no. 11: 682. https://doi.org/10.3390/f9110682

APA Style

Buajan, S., Liu, J., He, Z., & Feng, X. (2018). Effect of Gap Sizes on Specific Leaf Area and Chlorophyll Contents at the Castanopsis kawakamii Natural Reserve Forest, China. Forests, 9(11), 682. https://doi.org/10.3390/f9110682

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