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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.

References

  1. Abrams, M.D.; Kubiske, M.E. Leaf structural characteristics of 31 hardwood and conifer tree species in central Wisconsin: Influence of light regime and shade-tolerance rank. For. Ecol. Manag. 1990, 31, 245–253. [Google Scholar] [CrossRef]
  2. Brokaw, N.; Busing, R.T. Niche versus chance and tree diversity in forest gaps. Trends Ecol. Evol. 2000, 15, 183–188. [Google Scholar] [CrossRef]
  3. Brown, N. The implications of climate and gap microclimate for seedling growth conditions in a Bornean lowland rain forest. J. Trop. Ecol. 1993, 9, 153–168. [Google Scholar] [CrossRef]
  4. Wright, S.J.; Bunker, D.; Dalling, J.; Davies, S.; Díaz, S.; Engelbrecht, B.; Harms, K.; Kitajima, K.; Kraft, N.; Marks, C. Towards a functional trait based research program within the Center for Tropical Forest Science. 2006. Available online: https://s3.amazonaws.com/academia.edu.documents/41566891/Traits_report_20060817.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1540885648&Signature=faLhjslLAjf7vRyngv08Joy02j8%3D&response-content-disposition=inline%3B%20filename%3DTowards_a_functional_trait_based_researc.pdf (accessed on 30 October 2018).
  5. Swenson, N.G.; Enquist, B.J. Ecological and evolutionary determinants of a key plant functional trait: Wood density and its community-wide variation across latitude and elevation. Am. J. Bot. 2007, 94, 451–459. [Google Scholar] [CrossRef] [PubMed]
  6. Kunstler, G.; Lavergne, S.; Courbaud, B.; Thuiller, W.; Vieilledent, G.; Zimmermann, N.E.; Kattge, J.; Coomes, D.A. Competitive interactions between forest trees are driven by species’ trait hierarchy, not phylogenetic or functional similarity: Implications for forest community assembly. Ecol. Lett. 2012, 15, 831–840. [Google Scholar] [CrossRef] [PubMed]
  7. Roscher, C.; Schumacher, J.; Gubsch, M.; Lipowsky, A.; Weigelt, A.; Buchmann, N.; Schmid, B.; Schulze, E.-D. Using plant functional traits to explain diversity–productivity relationships. PLoS ONE 2012, 7, e36760. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Majeková, M.; de Bello, F.; Doležal, J.; Lepš, J. Plant functional traits as determinants of population stability. Ecology 2014, 95, 2369–2374. [Google Scholar] [CrossRef] [Green Version]
  9. Soliveres, S.; Maestre, F.T.; Bowker, M.A.; Torices, R.; Quero, J.L.; García-Gómez, M.; Cabrera, O.; Cea, A.P.; Coaguila, D.; Eldridge, D.J. Functional traits determine plant co-occurrence more than environment or evolutionary relatedness in global drylands. Perspect. Plant Ecol. Evol. Syst. 2014, 16, 164–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Lanta, V.; Klimešová, J.; Martincová, K.; Janeček, Š.; Doležal, J.; Rosenthal, J.; Lepš, J.; Klimeš, L. A test of the explanatory power of plant functional traits on the individual and population levels. Perspect. Plant Ecol. Evol. Syst. 2011, 13, 189–199. [Google Scholar] [CrossRef]
  11. Lienin, P.; Kleyer, M. Plant trait responses to the environment and effects on ecosystem properties. Basic Appl. Ecol. 2012, 13, 301–311. [Google Scholar] [CrossRef]
  12. Berner, L.T.; Law, B.E. Plant traits, productivity, biomass and soil properties from forest sites in the Pacific Northwest, 1999–2014. Sci. Data 2016, 3, 160002. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Zhu, J.; Lu, D.; Zhang, W. Effects of gaps on regeneration of woody plants: A meta-analysis. J. For. Res. 2014, 25, 501–510. [Google Scholar] [CrossRef]
  14. Markesteijn, L.; Poorter, L.; Bongers, F. Light-dependent leaf trait variation in 43 tropical dry forest tree species. Am. J. Bot. 2007, 94, 515–525. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Poorter, L.; Bongers, F. Leaf traits are good predictors of plant performance across 53 rain forest species. Ecology 2006, 87, 1733–1743. [Google Scholar] [CrossRef]
  16. Rijkers, T.; Pons, T.; Bongers, F. The effect of tree height and light availability on photosynthetic leaf traits of four neotropical species differing in shade tolerance. Funct. Ecol. 2000, 14, 77–86. [Google Scholar] [CrossRef] [Green Version]
  17. Shipley, B.; Vile, D.; Garnier, E.; Wright, I.; Poorter, H. Functional linkages between leaf traits and net photosynthetic rate: Reconciling empirical and mechanistic models. Funct. Ecol. 2005, 19, 602–615. [Google Scholar] [CrossRef]
  18. Oliva, M.A.; Kuki, K.N.; Mielke, M.S.; Ventrella, M.C.; Galvão, M.F.; Pinto, L.R. Key leaf traits indicative of photosynthetic plasticity in tropical tree species. Trees 2015, 29, 247–258. [Google Scholar] [CrossRef]
  19. Hoffmann, W.; Franco, A.; Moreira, M.; Haridasan, M. Specific leaf area explains differences in leaf traits between congeneric savanna and forest trees. Funct. Ecol. 2005, 19, 932–940. [Google Scholar] [CrossRef] [Green Version]
  20. Ordoñez, J.C.; Van Bodegom, P.M.; Witte, J.P.M.; Wright, I.J.; Reich, P.B.; Aerts, R. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 2009, 18, 137–149. [Google Scholar] [CrossRef] [Green Version]
  21. Wang, L.; Zhao, G.; Li, M.; Zhang, M.; Zhang, L.; Zhang, X.; An, L.; Xu, S. C:N:P Stoichiometry and Leaf Traits of Halophytes in an Arid Saline Environment, Northwest China. PLoS ONE 2015, 10, e0119935. [Google Scholar] [CrossRef] [PubMed]
  22. Cornelissen, J.; Lavorel, S.; Garnier, E.; Diaz, S.; Buchmann, N.; Gurvich, D.; Reich, P.; Ter Steege, H.; Morgan, H.; Van Der Heijden, M. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 2003, 51, 335–380. [Google Scholar] [CrossRef] [Green Version]
  23. Cheng, J.; Chu, P.; Chen, D.; Bai, Y. Functional correlations between specific leaf area and specific root length along a regional environmental gradient in Inner Mongolia grasslands. Funct. Ecol. 2016, 30, 985–997. [Google Scholar] [CrossRef]
  24. Rodriguez, H.G.; Maiti, R.; Kumari, C.A. Biodiversity of Leaf Traits in Woody Plant Species in Northeastern Mexico: A Synthesis. For. Res. 2016, 5, 169. [Google Scholar] [CrossRef]
  25. Green, B.R.; Durnford, D.G. The chlorophyll-carotenoid proteins of oxygenic photosynthesis. Annu. Rev. Plant Biol. 1996, 47, 685–714. [Google Scholar] [CrossRef] [PubMed]
  26. Yang, C.-M.; Lee, Y.-J. Seasonal changes of chlorophyll content in field-grown rice crops and their relationships with growth. Proc. Natl. Sci. Counc. Repub. China B Life Sci. 2001, 25, 233–238. [Google Scholar]
  27. Demey, A.; Staelens, J.; Baeten, L.; Boeckx, P.; Hermy, M.; Kattge, J.; Verheyen, K. Nutrient input from hemiparasitic litter favors plant species with a fast-growth strategy. Plant Soil 2013, 371, 53–66. [Google Scholar] [CrossRef]
  28. An, H.; Shangguan, Z. Generality of leaf traits relationships of dominant species along the secondary succession in the Loess Plateau of China. Afr. J. Biotechnol. 2012, 11, 1624–1631. [Google Scholar] [CrossRef]
  29. Hunt, R. Plant Growth Curves. The Functional Approach to Plant Growth Analysis; Edward Arnold Ltd.: London, UK, 1982. [Google Scholar]
  30. Hunt, R.; Cornelissen, J. Components of relative growth rate and their interrelations in 59 temperate plant species. New Phytol. 1997, 135, 395–417. [Google Scholar] [CrossRef] [Green Version]
  31. Buajan, S.; Liu, J.F.; He, Z.S.; Feng, X.P.; Muhammad, A.; Taimoor, H.F. Effect of Gap Size on The Dynamic of Micro Environments dring the Daytime at Castanopsis kawakamii Natural Reserve Forest, Sanming City, China. Environ. Nat. Resour. J. 2016, 14, 30–43. [Google Scholar] [CrossRef]
  32. Hu, L.L.; Zhu, J.J. Determination of the tridimensional shape of canopy gaps using two hemispherical photographs. Agric. For. Meteorol. 2009, 149, 862–872. [Google Scholar] [CrossRef]
  33. Vile, D.; Garnier, E.; Shipley, B.; Laurent, G.; Navas, M.-L.; Roumet, C.; Lavorel, S.; Diaz, S.; Hodgson, J.G.; Lloret, F. Specific leaf area and dry matter content estimate thickness in laminar leaves. Ann. Bot. 2005, 96, 1129–1136. [Google Scholar] [CrossRef] [PubMed]
  34. Garnier, E.; Shipley, B.; Roumet, C.; Laurent, G. A standardized protocol for the determination of specific leaf area and leaf dry matter content. Funct. Ecol. 2001, 15, 688–695. [Google Scholar] [CrossRef] [Green Version]
  35. Arnon, D.I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol. 1949, 24, 1. [Google Scholar] [CrossRef] [PubMed]
  36. Niklas, K.J.; Cobb, E.D.; Spatz, H.-C. Predicting the allometry of leaf surface area and dry mass. Am. J. Bot. 2009, 96, 531–536. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Aase, J. Relationship between leaf area and dry matter in winter wheat. Agron. J. 1978, 70, 563–565. [Google Scholar] [CrossRef]
  38. Milla, R.; Reich, P.B. The scaling of leaf area and mass: The cost of light interception increases with leaf size. Proc. R. Soc. Lond. B Biol. Sci. 2007, 274, 2109–2115. [Google Scholar] [CrossRef] [PubMed]
  39. Suoto, C.P.; Premoli, A.C.; Reich, P.B. Complex bioclimatic and soil gradients shape leaf trait variation in Embothrium coccineum (Proteaceae) among austral forests in Patagonia. Rev. Chilena Hist. Nat. 2009, 82, 209–222. [Google Scholar] [CrossRef]
  40. Karavin, N. Effects of leaf and plant age on specific leaf area in deciduous tree species Quercus cerris L. var. Cerris. Bangladesh J. Bot. 2014, 42, 301–306. [Google Scholar] [CrossRef]
  41. Wright, I.J.; Reich, P.B.; Cornelissen, J.H.; Falster, D.S.; Groom, P.K.; Hikosaka, K.; Lee, W.; Lusk, C.H.; Niinemets, Ü.; Oleksyn, J. Modulation of leaf economic traits and trait relationships by climate. Glob. Ecol. Biogeogr. 2005, 14, 411–421. [Google Scholar] [CrossRef]
  42. Givnish, T.J. Comparative studies of leaf form: Assessing the relative roles of selective pressures and phylogenetic constraints. New Phytol. 1987, 106, 131–160. [Google Scholar] [CrossRef]
  43. Xu, F.; Guo, W.; Xu, W.; Wei, Y.; Wang, R. Leaf morphology correlates with water and light availability: What consequences for simple and compound leaves? Prog. Nat. Sci. 2009, 19, 1789–1798. [Google Scholar] [CrossRef]
  44. Buajan, S.; Liu, J.F.; He, Z.S.; Feng, X.P.; Muhammad, A. The effect of light on micro-environment and specific leaf area within the gap, subtropical forest, China. Pak. J. Bot. 2017, 49, 273–282. [Google Scholar]
  45. Yulin, L.; Johnson, D.A.; Yongzhong, S.; Jianyuan, C.; Zhang, T. Specific leaf area and leaf dry matter content of plants growing in sand dunes. Bot. Bull. Acad. Sin. 2005, 46, 127–134. [Google Scholar]
  46. Niinemets, Ü.; Kull, K. Leaf structure vs. nutrient relationships vary with soil conditions in temperate shrubs and trees. Acta Oecol. 2003, 24, 209–219. [Google Scholar] [CrossRef]
  47. Fernandes, V.F.; Almeida, L.B.d.; Feijó, E.V.R.d.S.; Silva, D.d.C.; Oliveira, R.A.d.; Mielke, M.S.; Costa, L.C.d.B. Light intensity on growth, leaf micromorphology and essential oil production of Ocimum gratissimum. Revista Brasileira de Farmacognosia 2013, 23, 419–424. [Google Scholar] [CrossRef]
  48. Junfeng, W.; Yulong, F. The effect of light intensity on biomass allocation, leaf morphology and relative growth rate of two invasive plants. Acta Phytoecol. Sin. 2004, 28, 781–786. [Google Scholar]
  49. Stanton, K.M.; Weeks, S.S.; Dana, M.N.; Mickelbart, M.V. Light exposure and shade effects on growth, flowering, and leaf morphology of Spiraea alba du roi and Spiraea tomentosa L. HortScience 2010, 45, 1912–1916. [Google Scholar]
  50. Dalling, J.; Winter, K.; Hubbell, S. Variation in growth responses of neotropical pioneers to simulated forest gaps. Funct. Ecol. 2004, 18, 725–736. [Google Scholar] [CrossRef] [Green Version]
  51. Prado Júnior, J.; Schiavini, I.; Vale, V.; Lopes, S.; Arantes, C.; Oliveira, A. Functional leaf traits of understory species: Strategies to different disturbance severities. Braz. J. Biol. 2015, 75, 339–346. [Google Scholar] [CrossRef] [PubMed]
  52. Netto, A.T.; Campostrini, E.; de Oliveira, J.G.; Bressan-Smith, R.E. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Sci. Horticult. 2005, 104, 199–209. [Google Scholar] [CrossRef]
  53. Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160, 271–282. [Google Scholar] [CrossRef] [PubMed]
  54. Fleischer, W.E. The relation between chlorophyll content and rate of photosynthesis. J. Gen. Physiol. 1935, 18, 573–597. [Google Scholar] [CrossRef] [PubMed]
  55. Mathura, S.; Fossey, A.; Beck, S.L. Comparative study of chlorophyll content in diploid and tetraploid black wattle (Acacia mearnsii). Forestry 2006, 79, 381–388. [Google Scholar] [CrossRef]
  56. Srichaikul, B.; Bunsang, R.; Samappito, S.; Butkhup, L.; Bakker, G. Comparative Study of Chlorophyll Content in Leaves of Thai Morus alba Linn. Species. Plant Sci. Res. 2011, 3, 17–20. [Google Scholar] [CrossRef]
  57. Fan, X.; Zang, J.; Xu, Z.; Guo, S.; Jiao, X.; Liu, X.; Gao, Y. Effects of different light quality on growth, chlorophyll concentration and chlorophyll biosynthesis precursors of non-heading Chinese cabbage (Brassica campestris L.). Acta Physiol. Plant. 2013, 35, 2721–2726. [Google Scholar] [CrossRef]
  58. Jaleel, C.A.; Sankar, B.; Sridharan, R.; Panneerselvam, R. Soil salinity alters growth, chlorophyll content, and secondary metabolite accumulation in Catharanthus roseus. Turk. J. Biol. 2008, 32, 79–83. [Google Scholar]
  59. Bojović, B.; Marković, A. Correlation between nitrogen and chlorophyll content in wheat (Triticum aestivum L.). Kragujev. J. Sci. 2009, 31, 69–74. [Google Scholar]
  60. Hyyryläinen, A.; Rautio, P.; Turunen, M.; Huttunen, S. Seasonal and inter-annual variation in the chlorophyll content of three co-existing Sphagnum species exceeds the effect of solar UV reduction in a subarctic peatland. SpringerPlus 2015, 4, 1. [Google Scholar] [CrossRef] [PubMed]
  61. Houter, N.C.; Pons, T.L. Gap size effects on photoinhibition in understorey saplings in tropical rainforest. Plant Ecol. 2005, 179, 43–51. [Google Scholar] [CrossRef]
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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

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