Next Article in Journal
A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems
Next Article in Special Issue
Photosynthetic and Morphological Acclimation to High and Low Light Environments in Petasites japonicus subsp. giganteus
Previous Article in Journal
Pedunculate Oak Leaf Miners’ Community: Urban vs. Rural Habitat
Previous Article in Special Issue
Environmental Effects on Carbon Isotope Discrimination from Assimilation to Respiration in a Coniferous and Broad-Leaved Mixed Forest of Northeast China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 11
Issue 12
10.3390/f11121301
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Redeployment of Shoots into Better-Lit Positions within the Crowns of Saplings of Five Species with Different Growth Patterns

by
Kohei Koyama
1,2,*,†,
Hiroyuki Shirakawa
2,†,‡ and
Kihachiro Kikuzawa
2
1
Department of Life Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Inada-cho, Obihiro, Hokkaido 080-8555, Japan
2
Laboratory of Forest Biology, Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
*
Author to whom correspondence should be addressed.
†
These authors made equal contributions.
‡
Present address: Japan Broadcasting Corporation (NHK), Tokyo 150-8001, Japan.
Forests 2020, 11(12), 1301; https://doi.org/10.3390/f11121301
Submission received: 16 August 2020 / Revised: 30 November 2020 / Accepted: 30 November 2020 / Published: 3 December 2020
(This article belongs to the Special Issue Relationship between Forest Ecophysiology and Environment)
Browse Figures
Versions Notes

Abstract

:
Research Highlights: We demonstrate the first quantitative evidence that the shoot shedding of fast-growing species growing in a high-light environment is part of the process of shoot redeployment into better-lit outer parts of the crown. Background and Objectives: Light foraging by redeploying organs from shaded regions of a tree crown into better-lit regions is considered to apply to both leaves and shoots. To date, however, this hypothesis has never been tested for shoots. Materials and Methods: We investigated the shoot dynamics of saplings of five deciduous woody species. We included fast-growing and slow-growing species (Alnus sieboldiana Matsum., Castanea crenata Siebold & Zucc., Betula ermanii Cham., Acer distylum Siebold & Zucc., and Fagus crenata Blume). Results: Shoots in the shaded regions of the crowns of the fast-growing trees showed higher mortality rates than those at better-lit positions. Because of the selective shedding of the shaded shoots, at the end of the growth period the light environment experienced by the shoots that survived until the following spring was similar to that at the early stage of the same growth period. By contrast, the slow-growing trees displayed slow and determinate growth, with a very low mortality rate of shoots at all positions in the crown. Conclusions: The rapid shoot turnover of the fast-growing species resulted in the redeployment of shoots into better-lit positions within the tree crown in a manner similar to the redeployment of leaves.

Graphical Abstract

1. Introduction

A plant canopy is a dynamically changing system due to the continuous production, growth, and death of leaves and shoots [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]. The light environment within a canopy therefore changes temporally during plant growth [1,9,10,14,16]. Although studies on static plant form and function, such as allometric scaling approaches [17,18,19,20,21,22,23,24], are useful for analyzing time-averaged plant characteristics, investigations of plant organ dynamics are essential to understanding the development of individual plants and stands.
The dynamics of the aboveground parts of plants are driven by three processes: the production, growth, and death of leaves and shoots. Plants in different light environments differ in their shoot dynamics. In low-light environments (e.g., forest understories), plants may accelerate the shedding of their shaded, lower-positioned leaves or shoots because of their negative carbon balances [1] (but see [6]). For plants in open habitats, the continuous production of leaves into better-lit, higher positions in a canopy is an essential strategy to outcompete neighbors [25,26,27]. New leaves in the higher or outer positions of a canopy shade the leaves in the lower or inner part of the canopy [14,28,29,30]. Senescence and death of the shaded leaves of plants in high-light environments are a part of organ redeployment into better-lit positions [9,27,28,31,32,33,34,35,36,37,38,39]. Light foraging by organ redeployment from shaded regions into better-lit regions of a tree crown is considered to apply to shoots in addition to leaves [40,41]. To the best of our knowledge, however, no empirical study has tested the applicability of this hypothesis to shoots.
The temporal changes of a crown are determined by two-way interactions between the light environment and the dynamics of the leaves and shoots; light affects the dynamics (i.e., production, growth, and death) of organs, and the dynamics of organs affect the environment [1,4,5,8,10,14,42]. Shoot production rates are higher and shoot mortality rates are lower in better-lit, outer parts than in shaded, inner parts of the crown [13,40,43]. Furthermore, shoot growth and death depend on whether the shoots are shaded relative to other shoots throughout the entire crown, a phenomenon known as correlative inhibition [41,44,45,46,47]. Although these previous studies have successfully elucidated how the light environment affects shoot dynamics, they have clarified only the one-way effects of environmental factors on shoot dynamics. By contrast, only a few empirical studies (e.g., [6]) have quantified the effect of shoot dynamics on light environments, despite the importance of these effects having long been recognized in theoretical studies [1,4,5,8]. Therefore, further empirical studies of this two-way interaction are needed.
In this study, we investigated the two-way interactions between the within-crown light environment and shoot dynamics of saplings of five temperate, winter-deciduous woody plants that included fast- and slow-growing species grown in open, well-lit places. We tested the hypothesis that light foraging by organ redeployment from shaded regions into better-lit regions of a canopy, an idea well supported for leaves, also applies to shoots. We also expected that the interaction between shoot dynamics and light environments would be greater for fast-growing species than for slow-growing species because of their difference in shoot production and survival rates.

2. Materials and Methods

2.1. Study Species

We investigated saplings of five temperate, winter-deciduous tree species, all of which are native to Japan. The alder (Alnus sieboldiana Matsum.) is a nitrogen-fixing [48] pioneer species that grows in open habitats after disturbances [49]. Its higher photosynthetic rates than those of typical slow-growing species [50] indicate that this is a fast-growing species because the photosynthetic rates of leaves and whole-tree growth rates are positively correlated [51,52,53]. The Japanese chestnut (Castanea crenata Siebold & Zucc.) is an early-successional species that regenerates in sunny places [54]. In this study, we classified A. sieboldiana and C. crenata as fast-growing species based on their growth rates (see Results). The Russian rock birch (Betula ermanii Cham.) is an early-successional species, and its leaves have higher photosynthetic rates than those of typical late-successional species [55]. Thus, we classified B. ermanii as a medium-growth-rate species. Acer distylum Siebold & Zucc. is a shade-tolerant sub-canopy species [56] found in climax beech forests [57]. Although it can tolerate a shaded understory, this species also can grow rapidly in gaps [58]. Siebold’s beech (Fagus crenata Blume) is a shade-tolerant [59] and late-successional [55] species that dominates in cool-temperate climax forests in Japan [60]. The leaves of this species have lower photosynthetic rates than those of typical early-successional species [55,61,62,63,64]. A. distylum and F. crenata were classified herein as slow-growing species.

2.2. Study Sites and Sample Trees

The current study was conducted from 2002 to 2003 at two study sites in Japan. To ensure that the number of current-year shoots measured for each species exceeded 300, which we believed to be sufficient to describe species-specific patterns, we examined three to six saplings of each species (Table 1). Saplings of A. sieboldiana, C. crenata, and F. crenata were investigated at the experimental garden of, or a plantation adjacent to, the Center for Ecological Research (CER), Kyoto University (34°58′ N, 135°57′ E, 150 m asl). The mean annual temperature and precipitation at this location are 14.6 °C and 1618 mm, respectively (years 2006–2010; data from CER). Saplings of B. ermanii and A. distylum were investigated in a nursery at the Kamigamo Experimental Forest Station (KEFS), Kyoto University (35°04′ N, 135°46′ E, 140 m asl), which is approximately 21 km from CER. The mean annual temperature and precipitation at KEFS are 14.6 °C and 1580 mm, respectively (years 1971–2000; data from KEFS). Therefore, the two study sites had very similar temperatures and precipitation. All of the saplings investigated in the study were grown in open, well-lit places.

2.3. Shoot Census

For each sapling, we identified and periodically investigated all the current-year shoots that appeared in 2002 (Table 1, Figure 1). In 2002, we measured the length of each shoot after it had completely elongated. Shoot lengths (i.e., the annual shoot elongation) and the number of vegetative buds (i.e., the production of daughter shoots) were used as indices of the growth rates of each shoot. For A. sieboldiana, C. crenata, and A. distylum, we observed sylleptic shoots: lateral buds that grew from current-year shoots within the same year. All of these were regarded as part of their parent (proleptic) shoots, and the lengths and numbers of vegetative buds of the sylleptic shoots were summed with those of their parent to calculate the annual shoot elongation and bud production of the parent shoot. In April 2003, we evaluated the survival of each shoot. A shoot was defined as alive if it had at least one living bud (vegetative or flower bud). A shoot was defined as dead if it had been shed before April 2003 or if it lacked any living bud in April 2003.

2.4. Leaf Census

From bud break (late March 2002) to completion of leaf fall (late January 2003), we periodically investigated all leaves of the selected shoots of each species (Table 1) and recorded the numbers of leaves that appeared from these shoots every one or two weeks. The date of leaf emergence was postulated to be the midpoint between the date of a previous observation and the date that a new leaf was observed [65]. For each shoot, we calculated the shoot elongation period as the time between the emergence of the first and last leaf (i.e., the leaf emergence period calculated for each shoot). All leaves on the monitored shoots of F. crenata emerged as a flush within seven days between two successive census days (8 and 15 April 2002). The shoot elongation period of F. crenata was therefore estimated at 3.5 days.

2.5. Measurement of Light Environments

We measured the light environment within each crown for all the current-year shoots of the studied saplings. For the two fast-growing species (A. sieboldiana and C. crenata), which had indeterminate growth patterns, we investigated seasonal changes of the light environment within each canopy by conducting the same measurements twice during the same growth period in 2002 for all the shoots. The first measurement was taken in June or July, when almost all of the current-year proleptic shoots had emerged, but their elongation (including elongation of their sylleptic shoots) was not yet complete. The second measurement was conducted in September, when the elongation of almost all of the shoots was either complete or nearly so. For the other three medium- or slow-growing species, the measurements were conducted once, between June and September, when the elongation of most of the shoots was complete or nearly so (the dates of the measurements for each species are shown in the Results). We conducted the measurements from 11:00 to 13:00 local time on overcast days. On each measurement day, we used two quantum sensors (IKSX-7/101; Koito Manufacturing, Tokyo, Japan) to measure the photosynthetic photon flux density (PPFD) simultaneously. One sensor was fixed horizontally to a pole at the top of each tree. Light conditions at the tops of the trees in the open experimental gardens were nearly the same as those in the open full-lit location. Another sensor was fixed horizontally at the top of each shoot [46,66] with a hand-held measuring bar, as described in Muraoka et al. [67,68] (Figure 2). The investigators took care so as to not shade the sensor. We calculated the relative PPFD (rPPFD) as the ratio of the PPFD at the tip of each shoot to the PPFD at the top of the tree [46]. A previous study demonstrated that rPPFD measured on the horizontal surface at the tip of a branch represents the average light environment of that branch because it is highly correlated with the mean rPPFD of distal-to-proximal secondary shoots on that branch [46]. Therefore, we used the rPPFD at the tip of each shoot as a representative value for the average light environment of that shoot.
Some sylleptic shoots emerged after the first measurements (June–July) and were considered part of their parent proleptic shoots. Therefore, if a proleptic shoot had sylleptic shoots, the continuum of proleptic–sylleptic shoots of the same age (i.e., those shoots that emerged within the same growth period) was considered as one current-year shoot. In that case, the top of that current-year shoot was measured in the second measurement. In the case of a shoot that had been shed prior to the second measurements, we measured the PPFD at the approximate prior position of the shoot tip (estimated from its length).
We analyzed the effect of rPPFD that was obtained at the first measurement on each shoot for each species on the subsequent survival of that shoot in April 2003 using a generalized linear mixed model (GLMM) with the function glmer (binomial (link = “logit”)) [69] and R software v4.0.3 (Vienna, Austria) [70]:
ln y 1 − y = a + b x + r ⇔ y =   exp ( a + b x + r ) 1 + exp ( a + b x + r ) ⇔ y =   1 1 + exp ( − a − b x − r ) ,
where y is the survival of that shoot in April 2003 (0 = dead, 1 = survived), x is the fixed effect (rPPFD), a and b are the coefficients estimated by the glmer, and r is the random intercept (individual tree). The significance of the fixed effect of rPPFD (coefficient b) was tested by a likelihood ratio test with the R function anova (test = “Chisq”) by comparing Equation (1) with the null model with no rPPFD dependence (b = 0).
As mentioned, for the two fast-growing species, we conducted the PPFD measurements twice (i.e., the early (June or July) and the late (September) stages) in 2002 for the same set of shoots. The survival of the shoots was monitored until April 2003. The shoots were then assigned to one of two groups: (1) survivor group, those that survived until April 2003, or (2) dead group, those that had died by April 2003. We performed two types of comparisons (A and B) as follows. (A) Using all of the shoots from the survivor and dead groups, we compared the early versus late environments within the same growth period in 2002 within each tree crown. We expected that because of the rapid and indeterminate growth of the fast-growing species, the later-stage shoots would experience an inferior light environment compared with the early-stage shoots. This expected result would indicate that rapid growth causes an increment of self-shading. (B) We compared all the shoots at the early stage with the rPPFD of the survivor group shoots at the late stage. The survivor group shoots at the late stage were considered to be the shoots for the next year. Finally, we compared the difference in the results between (A) and (B). We hypothesized that any difference between (A) and (B) would indicate an effect of shoot shedding on the within-crown light environment. We expected that shoot deaths would counter the development of self-shading by the selective shedding of shaded shoots. For these two types of comparisons, we tested the differences in the two distributions of rPPFD (early versus late) with two-sample Kolmogorov–Smirnov tests using R software. All of the datasets used in this article are available online as Supplementary Materials.

3. Results

3.1. Growth Rate and Pattern

Table 2 summarizes the results of the growth and survival rates of the shoots. The shoot elongation period was longest for the fastest-growing species (A. sieboldiana), which showed typical indeterminate growth and successive emergence of leaves (Figure 3). By contrast, the elongation period was shortest for the slowest-growing species (F. crenata), which displayed typical determinate growth and a flush of leaf emergence during a very short time. The remaining species showed intermediate patterns. C. crenata experienced three flushes of leaf emergence, while B. ermanii and A. distylum showed one flush of emergence of early leaves and the successive emergence of late leaves.

3.2. Shoot Survival

The shoot survival rate was lower for the fast-growing species than for the slow-growing species (Table 2). The shoots in the shaded part of the canopy of the two fast-growing species (A. sieboldiana and C. crenata) experienced a high mortality rate (Figure 4, Table 2). By contrast, the shoots of the slow-growing species (A. distylum and F. crenata) experienced a low mortality rate at all positions within the canopy, regardless of the light environment (Figure 4, Table 2). The mortality rate of the shoots of the medium-growth-rate species (B. ermanii) was intermediate between these two extremes (Figure 4, Table 2). Logistic regression analysis demonstrated that those shoots located in a low-rPPFD environment were significantly less likely to survive than shoots located in a high-rPPFD environment in fast-growing (A. sieboldiana and C. crenata, p < 0.001) and medium-growth-rate species (B. ermanii, p < 0.001), but not in slow-growing species (A. distylum, p = 0.54; F. crenata, p = 0.45).

3.3. Light Environment

The long shoot elongation periods observed for the two fast-growing species caused the development of self-shading during a single growth period. Shoots of the two fast-growing species (A. sieboldiana and C. crenata) experienced significantly more self-shading in the late stage than the early stage of the same growth period (p < 0.001; Figure 5a). Nonetheless, the light environment in the later stage of growth experienced by the shoots that survived until April 2003 (i.e., the shoots for the next year) was similar to the one experienced by all the shoots in the early stage (Figure 5b). For both of the fast-growing species, the light distributions during the two stages did not differ significantly (A. sieboldiana, p = 0.48; C. crenata, p = 0.10; Figure 5b).

4. Discussion

The high metabolic activities of plant organs are generally associated with their fast turnover rates and short lifespans [71]. We observed that the shoot growth and mortality rates were higher for the fast-growing species than for the slow-growing species. Recent advances in plant ecophysiology have led to the concept of a “plant economics spectrum,” a covariation of a suite of traits that can be largely explained by a position on the single axis of fast versus slow strategies [71]. The core relationships in the economics spectrum are the negative correlations between trait values associated with productivity (e.g., higher growth rate, higher leaf photosynthetic rate) and those associated with persistence (e.g., slow turnover rate, mechanical stability, longer lifespan). This growth–persistence trade-off has been reported both at the organ level (e.g., “leaf economics spectrum” [72], “wood economics spectrum” [73], “root economics spectrum” [74]) and at the whole-plant level (fast vs. slow strategies or “plant economics spectrum”) [51,71,75,76,77], based on the rationale that more active organs afford individuals with better whole-plant growth rates at the expense of lower survival rates [1,51,73,75,78,79]. Such trade-offs have been found across different taxa, including different vascular plant groups [51,71,72,73,74,75,76,77,78], ferns [80], and mosses [81]. Similar trade-offs have also been observed in interspecific comparisons globally [72,73,82] and locally [55,83,84,85]. Analogous trade-offs have been quantified at intraspecific levels as well [86].
Two major theories have been proposed to explain the negative correlation between growth and survival rates. The first is the physiological constraints theory, which states that an organ or an individual cannot attain both high productivity and high persistence [87,88]. Such a trade-off may arise because of the selective investment of resources between traits related to high activity and those related to persistence [71,75,89]. For example, an investment of nutrients into the photosynthetic apparatus enhances photosynthetic rates [90] at the expense of investment in defense chemicals that reduce herbivory [75] or into mechanical toughness [91]. Likewise, stems comprising dense wood with narrow conduits are durable but may be less effective at transport [88] (but see [73]). Thus far, the physiological constraints underlying growth–persistence trade-offs have been intensively studied both theoretically [92] and empirically [73,91,93]. The second explanation for the negative correlation between growth and survival rates is the theory of optimal longevity (TOL): an organ should be replaced at an optimal time to maximize whole-plant carbon gain [9,29,31,38,39,40,94,95,96]. The key idea is that plant organs should be continuously moved into more productive environments [9,29,31,39,40,71,97] because a plant generally competes with its neighbors for light [25,26,71]. The production and deployment of new leaves and shoots into better-lit positions cause self-shading of the shaded inner part of the plant canopy [1,28,36] resulting in the death of the shaded leaves or shoots [1,3,9,29,30,39,97]. Such organ redeployment would be faster if the plants or shoots were growing faster [3,9,14,31,36,65,97]. TOL predicts that a higher growth rate is one of the reasons for a shorter organ lifespan. The two theories are not mutually exclusive, and the difference between them is a difference between proximate and ultimate or evolutionary factors [98].
Previous studies of shoots, however, have focused primarily on testing physiological constraints (i.e., causes of short organ lifespan), whereas few studies [13,40] have directly investigated the consequences of a shortened organ lifespan. Specifically, although the concept of the economics spectrum may apply to any organ [71], quantitative assessment of the TOL is limited to leaves [39,99] or fine roots [100]. It has been suggested that TOL can also be applied to shoots [40,101]; however, those studies did not quantitatively evaluate the effects of shoot shedding on light environments within crowns. Our results quantitatively demonstrated a consequence of rapid shoot shedding; i.e., redeployment of shoots into better-lit positions within a tree crown. The present results are consistent with the predictions of TOL, which, to date, have been thoroughly tested with leaves but not with shoots.
Based on the wood economics spectrum theory [73], we analyzed the wood density of four of the five species from the published literature and reconfirmed the predicted relationship [73] that the shoot survival rate is higher for species with higher wood density (Figure 6). A recent study [102] further proposed that a variation in crown structure (e.g., those maximizing light capture vs. those maximizing vertical growth) can be considered a new trait trade-off continuum: the structural economics spectrum [102]. This spectrum can be integrated with the previously described concepts of the leaf and wood economics spectra [102], in which the turnover rate of organs (i.e., leaf lifespan) plays a central role as an indicator of plant strategy [71,72,73]. Quantitative results regarding the consequence of organ turnover on the crown structure and light environment, such as those presented in this study, would be useful for investigating the suggested linkage among those economics spectra in future studies.
A simulation that was based on a dynamic functional-structural model [1] suggests that the death of shade shoots among shaded understory saplings is merely a passive response to the negative carbon balance of shaded shoots. Later, Laurans and Vincent [6] conducted an empirical study to test that suggestion. Their results showed that shoot shedding was not accelerated, whereas shoot production was suppressed for saplings in more heavily shaded understories than the conspecific individuals in less-shaded gaps. Laurans and Vincent [6] intentionally excluded pioneers that grew only in open habitats from their experimental design because their objective was to clarify how the suppression of shoot production in the shade and the long lifespan of the branches of shade-tolerant species determined crown morphological variation as a mechanism of shade tolerance. However, neither of those previous studies [1,6] clarified the consequences of shoot shedding on the within-crown light environment of vigorously growing trees in open, well-lit places. In the present study, we focused on plants in high-light environments, in which the continuous production and growth of shoots are expected. Our results, therefore, cannot be directly compared with those of these previous studies. In contrast to the cases of trees, there have been many results from experiments on herbaceous species grown in well-lit places that clarified the consequences of leaf shedding on the within-canopy light environment (e.g., [9,28,29,30,39]). These studies reported the function of the redeployment of leaves from shaded into well-lit places. Thus, our results of the shoot redeployment process observed in open, well-lit experimental gardens are more similar to the results of herbaceous species in open places than to the results of woody species in shaded understories.
Our study had several limitations. First, we monitored the trees for only a short time. The shoot mortality rate of the slow-growing trees was very low during the single growth period investigated in this study. It has been reported that late-successional old trees maintain their within-canopy structure by continuously replacing branches [105]. The implication may therefore be that fast- and slow-growing species maintain their within-crown light environments by replacing shoots rapidly and slowly, respectively. Further studies that monitor trees for more extended periods of time are therefore needed. Second, competition with neighbors was excluded in our experiment, which involved growing each sapling in an isolated condition. Nonetheless, the development of a tree crown in a real stand is also affected by competition with neighboring trees [102,106,107] and shading by a surrounding forest canopy [1,4,6,8]. Further studies are therefore needed before the present results can be generalized to real forest conditions.

5. Conclusions

We demonstrated the first empirical evidence that production and shedding of shoots for the saplings of fast-growing woody plants in high-light environments are part of a process of the redeployment of shoots into better-lit parts of the crown, similar to the redeployment of leaves. By contrast, the slow-growing trees displayed slow and determinate growth, with a low mortality rate of the shoots at all positions in the crown. These results indicate that fast- and slow-growing species maintain their within-crown light environments by replacing shoots rapidly and slowly, respectively.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/11/12/1301/s1.

Author Contributions

Conceptualization, K.K. (Kohei Koyama), H.S., and K.K. (Kihachiro Kikuzawa); formal analysis, K.K. (Kohei Koyama) and H.S.; investigation, H.S.; methodology, H.S.; writing—original draft, K.K. (Kohei Koyama) and H.S.; writing—review and editing, K.K. (Kohei Koyama), H.S., and K.K. (Kihachiro Kikuzawa). All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Japan Society for the Promotion of Science (KAKENHI Grant Number 18K06406).

Acknowledgments

We thank Maki Suzuki, Adriano Dal Bosco, Martin Lechowicz, Atsushi Takayanagi, and Michimasa Yamasaki for their useful comments. We also thank Iwao Kojima for assistance in the field experiments. The present study was conducted with Joint Usage of the Center for Ecological Research (2018jurc-cer27), Kyoto University. The data may be used with proper citation of this article without contacting the authors.

Conflicts of Interest

The authors declare no conflict of interest.

Data Availability Statement

The data presented in this study are available in the Supplementary Materials.

References

  1. Sterck, F.J.; Schieving, F.; Lemmens, A.; Pons, T.L. Performance of trees in forest canopies: Explorations with a bottom-up functional-structural plant growth model. New Phytol. 2005, 166, 827–843. [Google Scholar] [CrossRef]
  2. Bazzaz, F.A.; Harper, J.L. Demographic analysis of the growth of Linum usitatissimum. New Phytol. 1977, 78, 193–208. [Google Scholar] [CrossRef]
  3. Niinemets, U.; Lukjanova, A. Total foliar area and average leaf age may be more strongly associated with branching frequency than with leaf longevity in temperate conifers. New Phytol. 2003, 158, 75–89. [Google Scholar] [CrossRef]
  4. Sterck, F.J.; Schieving, F. 3-D growth patterns of trees: Effects of carbon economy, meristem activity, and selection. Ecol. Monogr. 2007, 77, 405–420. [Google Scholar] [CrossRef]
  5. Beyer, R.; Letort, V.; Cournede, P.H. Modeling tree crown dynamics with 3D partial differential equations. Front. Plant Sci. 2014, 5, 329. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Laurans, M.; Vincent, G. Are inter- and intraspecific variations of sapling crown traits consistent with a strategy promoting light capture in tropical moist forest? Ann. Bot. 2016, 118, 983–996. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Koyama, K.; Yamamoto, K.; Ushio, M. A lognormal distribution of the lengths of terminal twigs on self-similar branches of elm trees. Proc. R. Soc. B Biol. Sci. 2017, 284, 20162395. [Google Scholar] [CrossRef] [Green Version]
  8. Rani, R.; Abramowicz, K.; Falster, D.S.; Sterck, F.; Brannstrom, A. Effects of bud-flushing strategies on tree growth. Tree Physiol. 2018, 38, 1384–1393. [Google Scholar] [CrossRef]
  9. Hikosaka, K. A model of dynamics of leaves and nitrogen in a plant canopy: An integration of canopy photosynthesis, leaf life span, and nitrogen use efficiency. Am. Nat. 2003, 162, 149–164. [Google Scholar] [CrossRef] [Green Version]
  10. Hikosaka, K. Leaf canopy as a dynamic system: Ecophysiology and optimality in leaf turnover. Ann. Bot. 2005, 95, 521–533. [Google Scholar] [CrossRef] [Green Version]
  11. Kaitaniemi, P.; Lintunen, A.; Sievänen, R. Power-law estimation of branch growth. Ecol. Model. 2020, 416, 108900. [Google Scholar] [CrossRef]
  12. Wang, F.; Kang, M.; Lu, Q.; Letort, V.; Han, H.; Guo, Y.; de Reffye, P.; Li, B. A stochastic model of tree architecture and biomass partitioning: Application to Mongolian Scots pines. Ann. Bot. 2011, 107, 781–792. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Shirakawa, H.; Kikuzawa, K. Crown hollowing as a consequence of early shedding of leaves and shoots. Ecol. Res. 2009, 24, 839–845. [Google Scholar] [CrossRef]
  14. Koyama, K.; Kikuzawa, K. Is whole-plant photosynthetic rate proportional to leaf area? A test of scalings and a logistic equation by leaf demography census. Am. Nat. 2009, 173, 640–649. [Google Scholar] [CrossRef] [PubMed]
  15. Koyama, K.; Hidaka, Y.; Ushio, M. Dynamic scaling in the growth of a non-branching plant, Cardiocrinum cordatum. PLoS ONE 2012, 7, e45317. [Google Scholar] [CrossRef]
  16. Ishii, H.; Asano, S. The role of crown architecture, leaf phenology and photosynthetic activity in promoting complementary use of light among coexisting species in temperate forests. Ecol. Res. 2010, 25, 715–722. [Google Scholar] [CrossRef]
  17. Bentley, L.P.; Stegen, J.C.; Savage, V.M.; Smith, D.D.; von Allmen, E.I.; Sperry, J.S.; Reich, P.B.; Enquist, B.J. An empirical assessment of tree branching networks and implications for plant allometric scaling models. Ecol. Lett. 2013, 16, 1069–1078. [Google Scholar] [CrossRef]
  18. Smith, D.D.; Sperry, J.S.; Enquist, B.J.; Savage, V.M.; McCulloh, K.A.; Bentley, L.P. Deviation from symmetrically self-similar branching in trees predicts altered hydraulics, mechanics, light interception and metabolic scaling. New Phytol. 2014, 201, 217–229. [Google Scholar] [CrossRef]
  19. Savage, V.M.; Bentley, L.P.; Enquist, B.J.; Sperry, J.S.; Smith, D.D.; Reich, P.B.; von Allmen, E.I. Hydraulic trade-offs and space filling enable better predictions of vascular structure and function in plants. Proc. Natl. Acad. Sci. USA 2010, 107, 22722–22727. [Google Scholar] [CrossRef] [Green Version]
  20. Enquist, B.J.; Bentley, L.P.; Shenkin, A.; Maitner, B.; Savage, V.; Michaletz, S.; Blonder, B.; Buzzard, V.; Espinoza, T.E.B.; Farfan-Rios, W.; et al. Assessing trait-based scaling theory in tropical forests spanning a broad temperature gradient. Glob. Ecol. Biogeogr. 2017, 26, 1357–1373. [Google Scholar] [CrossRef]
  21. Kurosawa, Y.; Mori, S.; Wang, M.; Ferrio, J.P.; Yamaji, K.; Koyama, K.; Haruma, T.; Doyama, K. Initial burst of root development with decreasing respiratory carbon cost in Fagus crenata Blume seedlings. Plant Spec. Biol. 2020. [Google Scholar] [CrossRef]
  22. Martin-Ducup, O.; Ploton, P.; Barbier, N.; Momo Takoudjou, S.; Mofack, G., II; Kamdem, N.G.; Fourcaud, T.; Sonké, B.; Couteron, P.; Pélissier, R. Terrestrial laser scanning reveals convergence of tree architecture with increasingly dominant crown canopy position. Funct. Ecol. 2020. [Google Scholar] [CrossRef]
  23. Lau, A.; Martius, C.; Bartholomeus, H.; Shenkin, A.; Jackson, T.; Malhi, Y.; Herold, M.; Bentley, L.P. Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling. For. Ecol. Manag. 2019, 439, 132–145. [Google Scholar] [CrossRef]
  24. Komiyama, A.; Nakagawa, M.; Kato, S. Common allometric relationships for estimating tree biomasses in cool temperate forests of Japan. J. Jpn. For. Soc. 2011, 93, 220–225. [Google Scholar] [CrossRef] [Green Version]
  25. Givnish, T.J. On the adaptive significance of leaf height in forest herbs. Am. Nat. 1982, 120, 353–381. [Google Scholar] [CrossRef]
  26. Anten, N.P.R.; During, H.J. Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion? Oecologia 2011, 167, 293–303. [Google Scholar] [CrossRef] [Green Version]
  27. Boonman, A.; Anten, N.P.R.; Dueck, T.A.; Jordi, W.; van der Werf, A.; Voesenek, L.; Pons, T.L. Functional significance of shade-induced leaf senescence in dense canopies: An experimental test using transgenic tobacco. Am. Nat. 2006, 168, 597–607. [Google Scholar] [CrossRef]
  28. Field, C. Allocating leaf nitrogen for the maximization of carbon gain—Leaf age as a control on the allocation program. Oecologia 1983, 56, 341–347. [Google Scholar] [CrossRef]
  29. Oikawa, S.; Hikosaka, K.; Hirose, T. Leaf lifespan and lifetime carbon balance of individual leaves in a stand of an annual herb, Xanthium canadense. New Phytol. 2006, 172, 104–116. [Google Scholar] [CrossRef]
  30. Oikawa, S.; Hikosaka, K.; Hirose, T. Dynamics of leaf area and nitrogen in the canopy of an annual herb, Xanthium canadense. Oecologia 2005, 143, 517–526. [Google Scholar] [CrossRef]
  31. Kikuzawa, K. A cost-benefit analysis of leaf habit and leaf longevity of trees and their geographical pattern. Am. Nat. 1991, 138, 1250–1263. [Google Scholar] [CrossRef]
  32. Hikosaka, K.; Terashima, I.; Katoh, S. Effects of leaf age, nitrogen nutrition and photon flux-density on the distribution of nitrogen among leaves of a vine (Ipomoea tricolor Cav.) grown horizontally to avoid mutual shading of leaves. Oecologia 1994, 97, 451–457. [Google Scholar] [CrossRef] [PubMed]
  33. Hikosaka, K. Effects of leaf age, nitrogen nutrition and photon flux density on the organization of the photosynthetic apparatus in leaves of a vine (Ipomoea tricolor Cav.) grown horizontally to avoid mutual shading of leaves. Planta 1996, 198, 144–150. [Google Scholar] [CrossRef]
  34. Franklin, O.; Ågren, G.I. Leaf senescence and resorption as mechanisms of maximizing photosynthetic production during canopy development at N limitation. Funct. Ecol. 2002, 16, 727–733. [Google Scholar] [CrossRef] [Green Version]
  35. Kikuzawa, K.; Lechowicz, M.J. Theories of Leaf Longevity. In Ecology of Leaf Longevity; Kikuzawa, K., Lechowicz, M.J., Eds.; Springer: Tokyo, Japan, 2011; pp. 41–56. [Google Scholar]
  36. Osada, N.; Takeda, H.; Kitajima, K.; Pearcy, R.W. Functional correlates of leaf demographic response to gap release in saplings of a shade-tolerant tree, Elateriospermum tapos. Oecologia 2003, 137, 181–187. [Google Scholar] [CrossRef]
  37. Escudero, A.; Mediavilla, S. Decline in photosynthetic nitrogen use efficiency with leaf age and nitrogen resorption as determinants of leaf life span. J. Ecol. 2003, 91, 880–889. [Google Scholar] [CrossRef]
  38. Tanaka, T.; Oikawa, S.; Kurokawa, C. Leaf shedding increases the photosynthetic rate of the canopy in N2-fixing and non-N2-fixing woody species. Tree Physiol. 2018, 38, 1903–1911. [Google Scholar] [CrossRef]
  39. Oikawa, S.; Hikosaka, K.; Hirose, T. Does leaf shedding increase the whole-plant carbon gain despite some nitrogen being lost with shedding? New Phytol. 2008, 178, 617–624. [Google Scholar] [CrossRef]
  40. Seiwa, K.; Kikuzawa, K.; Kadowaki, T.; Akasaka, S.; Ueno, N. Shoot life span in relation to successional status in deciduous broad-leaved tree species in a temperate forest. New Phytol. 2006, 169, 537–548. [Google Scholar] [CrossRef]
  41. Stoll, P.; Schmid, B. Plant foraging and dynamic competition between branches of Pinus sylvestris in contrasting light environments. J. Ecol. 1998, 86, 934–945. [Google Scholar] [CrossRef]
  42. Umeki, K.; Kikuzawa, K.; Sterck, F.J. Influence of foliar phenology and shoot inclination on annual photosynthetic gain in individual beech saplings: A functional–structural modeling approach. For. Ecol. Manag. 2010, 259, 2141–2150. [Google Scholar] [CrossRef]
  43. Umeki, K.; Seino, T.; Lim, E.-M.; Honjo, T. Patterns of shoot mortality in Betula platyphylla in northern Japan. Tree Physiol. 2006, 26, 623–632. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Takenaka, A. Shoot growth responses to light microenvironment and correlative inhibition in tree seedlings under a forest canopy. Tree Physiol. 2000, 20, 987–991. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Sugiura, D.; Tateno, M. Concentrative nitrogen allocation to sun-lit branches and the effects on whole-plant growth under heterogeneous light environments. Oecologia 2013, 172, 949–960. [Google Scholar] [CrossRef]
  46. Chen, L.; Sumida, A. Effects of light on branch growth and death vary at different organization levels of branching units in Sakhalin spruce. Trees 2018, 32, 1123–1134. [Google Scholar] [CrossRef]
  47. Sprugel, D.G. When branch autonomy fails: Milton’s Law of resource availability and allocation. Tree Physiol. 2002, 22, 1119–1124. [Google Scholar] [CrossRef] [Green Version]
  48. Yamanaka, T.; Okabe, H.; Kawai, S. Growth and nodulation in Alnus sieboldiana in response to Frankia inoculation and nitrogen treatments. Trees 2016, 30, 539–544. [Google Scholar] [CrossRef]
  49. Kamijo, T.; Kitayama, K.; Sugawara, A.; Urushimichi, S.; Sasai, K. Primary succession of the warm-temperate broad-leaved forest on a volcanic island, Miyake-jima, Japan. Folia Geobot. 2002, 37, 71–91. [Google Scholar] [CrossRef]
  50. Kikuzawa, K. Phenological and morphological adaptations to the light environment in two woody and two herbaceous plant species. Funct. Ecol. 2003, 17, 29–38. [Google Scholar] [CrossRef]
  51. 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]
  52. Gray, E.F.; Wright, I.J.; Falster, D.S.; Eller, A.S.D.; Lehmann, C.E.R.; Bradford, M.G.; Cernusak, L.A. Leaf:wood allometry and functional traits together explain substantial growth rate variation in rainforest trees. AoB Plants 2019, 11, plz024. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Wright, I.J.; Cooke, J.; Cernusak, L.A.; Hutley, L.B.; Scalon, M.C.; Tozer, W.C.; Lehmann, C.E.R. Stem diameter growth rates in a fire-prone savanna correlate with photosynthetic rate and branch-scale biomass allocation, but not specific leaf area. Austral Ecol. 2019, 44, 339–350. [Google Scholar] [CrossRef]
  54. Seiwa, K.; Watanabe, A.; Irie, K.; Kanno, H.; Saitoh, T.; Akasaka, S. Impact of site-induced mouse caching and transport behaviour on regeneration in Castanea crenata. J. Veg. Sci. 2002, 13, 517–526. [Google Scholar] [CrossRef]
  55. Koike, T. Leaf structure and photosynthetic performance as related to the forest succession of deciduous broad-leaved trees. Plant Spec. Biol. 1988, 3, 77–87. [Google Scholar] [CrossRef]
  56. Cao, K.F.; Ohkubo, T. Allometry, root/shoot ratio and root architecture in understory saplings of deciduous dicotyledonous trees in central Japan. Ecol. Res. 1998, 13, 217–227. [Google Scholar] [CrossRef]
  57. Ohkubo, T.; Kaji, M.; Hamaya, T. Structure of primary Japanese beech (Fagus japonica maxim.) forests in the Chichibu Mountains, central Japan, with special reference to regeneration processes. Ecol. Res. 1988, 3, 101–116. [Google Scholar] [CrossRef]
  58. Sakai, S. Patterns of branching and extension growth of vigorous saplings of Japanese Acer species in relation to their regeneration strategies. Can. J. Bot. 1987, 65, 1578–1585. [Google Scholar] [CrossRef]
  59. Miyashita, A.; Tateno, M. A novel index of leaf RGR predicts tree shade tolerance. Funct. Ecol. 2014, 28, 1321–1329. [Google Scholar] [CrossRef] [Green Version]
  60. Okaura, T.; Harada, K. Phylogeographical structure revealed by chloroplast DNA variation in Japanese beech (Fagus crenata Blume). Heredity 2002, 88, 322. [Google Scholar] [CrossRef] [Green Version]
  61. Koyama, K.; Kikuzawa, K. Reduction of photosynthesis before midday depression occurred: Leaf photosynthesis of Fagus crenata in a temperate forest in relation to canopy position and a number of days after rainfall. Ecol. Res. 2011, 26, 999–1006. [Google Scholar] [CrossRef]
  62. Koyama, K.; Kikuzawa, K. Can we estimate forest gross primary production from leaf lifespan? A test in a young Fagus crenata forest. J. Ecol. Field Biol. 2010, 33, 253–260. [Google Scholar] [CrossRef] [Green Version]
  63. Koyama, K.; Kikuzawa, K. Geometrical similarity analysis of photosynthetic light response curves, light saturation and light use efficiency. Oecologia 2010, 164, 53–63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Watanabe, M.; Hiroshima, H.; Kinose, Y.; Okabe, S.; Izuta, T. Nitrogen use efficiency for growth of Fagus crenata seedlings under elevated ozone and different soil nutrient conditions. Forests 2020, 11, 371. [Google Scholar] [CrossRef] [Green Version]
  65. Koyama, K.; Kikuzawa, K. Intraspecific variation in leaf life span for the semi-evergreen liana Akebia trifoliata is caused by both seasonal and aseasonal factors in a temperate forest. J. Ecol. Field Biol. 2008, 31, 207–211. [Google Scholar] [CrossRef] [Green Version]
  66. Hollinger, D.Y. Optimality and nitrogen allocation in a tree canopy. Tree Physiol. 1996, 16, 627–634. [Google Scholar] [CrossRef] [Green Version]
  67. Muraoka, H.; Hirota, H.; Matsumoto, J.; Nishimura, S.; Tang, Y.; Koizumi, H.; Washitani, I. On the convertibility of different microsite light availability indices, relative illuminance and relative photon flux density. Funct. Ecol. 2001, 15, 798–803. [Google Scholar] [CrossRef]
  68. Muraoka, H.; Tang, Y.; Koizumi, H.; Washitani, I. Combined effects of light and water availability on photosynthesis and growth of Arisaema heterophyllum in the forest understory and an open site. Oecologia 1997, 112, 26–34. [Google Scholar] [CrossRef]
  69. Bates, D.; Mächler, M.; Bolker, B.; Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 2015, 67, 48. [Google Scholar] [CrossRef]
  70. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020. [Google Scholar]
  71. Reich, P.B. The world-wide ‘fast–slow’ plant economics spectrum: A traits manifesto. J. Ecol. 2014, 102, 275–301. [Google Scholar] [CrossRef]
  72. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T.; Cornelissen, J.H.; Diemer, M.; et al. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef]
  73. Chave, J.; Coomes, D.; Jansen, S.; Lewis, S.L.; Swenson, N.G.; Zanne, A.E. Towards a worldwide wood economics spectrum. Ecol. Lett. 2009, 12, 351–366. [Google Scholar] [CrossRef] [PubMed]
  74. Roumet, C.; Birouste, M.; Picon-Cochard, C.; Ghestem, M.; Osman, N.; Vrignon-Brenas, S.; Cao, K.-f.; Stokes, A. Root structure–function relationships in 74 species: Evidence of a root economics spectrum related to carbon economy. New Phytol. 2016, 210, 815–826. [Google Scholar] [CrossRef] [PubMed]
  75. Coley, P.D.; Bryant, J.P.; Chapin, F.S. Resource availability and plant antiherbivore defense. Science 1985, 230, 895–899. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Lambers, H.; Poorter, H. Inherent variation in growth rate between higher plants: A search for physiological causes and ecological consequences. Adv. Ecol. Res. 1992, 23, 187–261. [Google Scholar]
  77. Freschet, G.T.; Cornelissen, J.H.C.; van Logtestijn, R.S.P.; Aerts, R. Evidence of the ‘plant economics spectrum’ in a subarctic flora. J. Ecol. 2010, 98, 362–373. [Google Scholar] [CrossRef] [Green Version]
  78. Iida, Y.; Sun, I.F.; Price, C.A.; Chen, C.T.; Chen, Z.S.; Chiang, J.M.; Huang, C.L.; Swenson, N.G. Linking leaf veins to growth and mortality rates: An example from a subtropical tree community. Ecol. Evol. 2016, 6, 6085–6096. [Google Scholar] [CrossRef]
  79. Wright, S.J.; Kitajima, K.; Kraft, N.J.B.; Reich, P.B.; Wright, I.J.; Bunker, D.E.; Condit, R.; Dalling, J.W.; Davies, S.J.; Díaz, S.; et al. Functional traits and the growth-mortality trade-off in tropical trees. Ecology 2010, 91, 3664–3674. [Google Scholar] [CrossRef]
  80. Karst, A.L.; Lechowicz, M.J. Are correlations among foliar traits in ferns consistent with those in the seed plants? New Phytol. 2007, 173, 306–312. [Google Scholar] [CrossRef]
  81. Waite, M.; Sack, L. How does moss photosynthesis relate to leaf and canopy structure? Trait relationships for 10 Hawaiian species of contrasting light habitats. New Phytol. 2010, 185, 156–172. [Google Scholar] [CrossRef]
  82. Reich, P.B.; Walters, M.B.; Ellsworth, D.S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl. Acad. Sci. USA 1997, 94, 13730–13734. [Google Scholar] [CrossRef] [Green Version]
  83. Reich, P.B.; Walters, M.B.; Ellsworth, D.S. Leaf age and season influence the relationships between leaf nitrogen, leaf mass per area and photosynthesis in maple and oak trees. Plant Cell Environ. 1991, 14, 251–259. [Google Scholar] [CrossRef]
  84. Shiodera, S.; Rahajoe, J.S.; Kohyama, T. Variation in longevity and traits of leaves among co-occurring understorey plants in a tropical montane forest. J. Trop. Ecol. 2008, 24, 121–133. [Google Scholar] [CrossRef]
  85. Saura-Mas, S.; Shipley, B.; Lloret, F. Relationship between post-fire regeneration and leaf economics spectrum in Mediterranean woody species. Funct. Ecol. 2009, 23, 103–110. [Google Scholar] [CrossRef]
  86. Vincent, G. Leaf life span plasticity in tropical seedlings grown under contrasting light regimes. Ann. Bot. 2006, 97, 245–255. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Reich, P.; Walters, M.; Ellsworth, D. Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol. Monogr. 1992, 62, 365–392. [Google Scholar] [CrossRef]
  88. Baas, P.; Ewers, F.W.; Davis, S.D.; Wheeler, E.A. Evolution of xylem physiology. In The Evolution of Plant Physiology; Hemsley, A.R., Poole, I., Eds.; Elsevier: Amsterdam, The Netherlands, 2004; pp. 273–295. [Google Scholar]
  89. Rosas, T.; Mencuccini, M.; Barba, J.; Cochard, H.; Saura-Mas, S.; Martínez-Vilalta, J. Adjustments and coordination of hydraulic, leaf and stem traits along a water availability gradient. New Phytol. 2019, 223, 632–646. [Google Scholar] [CrossRef]
  90. Mooney, H.A.; Gulmon, S.L. Environmental and evolutionary constraints on the photosynthetic characteristics of higher plants. In Topics in Plant Population Biology; Solbrig, O.T., Jain, S., Johnson, G.B., Raven, P.H., Eds.; Macmillan Education: London, UK, 1979; pp. 316–337. [Google Scholar]
  91. Onoda, Y.; Westoby, M.; Adler, P.B.; Choong, A.M.; Clissold, F.J.; Cornelissen, J.H.; Diaz, S.; Dominy, N.J.; Elgart, A.; Enrico, L.; et al. Global patterns of leaf mechanical properties. Ecol. Lett. 2011, 14, 301–312. [Google Scholar] [CrossRef] [Green Version]
  92. Shipley, B.; Lechowicz, M.J.; Wright, I.; Reich, P.B. Fundamental trade-offs generating the worldwide leaf economics spectrum. Ecology 2006, 87, 535–541. [Google Scholar] [CrossRef]
  93. Niinemets, Ü.; Díaz-Espejo, A.; Flexas, J.; Galmés, J.; Warren, C.R. Role of mesophyll diffusion conductance in constraining potential photosynthetic productivity in the field. J. Exp. Bot. 2009, 60, 2249–2270. [Google Scholar] [CrossRef] [Green Version]
  94. Chabot, B.F.; Hicks, D.J. The ecology of leaf life spans. Ann. Rev. Ecol. Syst. 1982, 13, 229–259. [Google Scholar] [CrossRef]
  95. Osada, N.; Oikawa, S.; Kitajima, K. Implications of life span variation within a leaf cohort for evaluation of the optimal timing of leaf shedding. Funct. Ecol. 2015, 29, 308–314. [Google Scholar] [CrossRef] [Green Version]
  96. Oikawa, S.; Suno, K.; Osada, N. Inconsistent intraspecific pattern in leaf life span along nitrogen-supply gradient. Am. J. Bot. 2017, 104, 342–346. [Google Scholar] [CrossRef] [PubMed]
  97. Osada, N.; Takeda, H.; Furukawa, A.; Awang, M. Leaf dynamics and maintenance of tree crowns in a Malaysian rain forest stand. J. Ecol. 2001, 89, 774–782. [Google Scholar] [CrossRef]
  98. Reich, P.B.; Wright, I.J.; Cavender-Bares, J.; Craine, J.M.; Oleksyn, J.; Westoby, M.; Walters, M.B. The evolution of plant functional variation: Traits, spectra, and strategies. Int. J. Plant Sci. 2003, 164, S143–S164. [Google Scholar] [CrossRef]
  99. Kikuzawa, K.; Ackerly, D. Significance of leaf longevity in plants. Plant Spec. Biol. 1999, 14, 39–45. [Google Scholar] [CrossRef]
  100. Eissenstat, D.; Wells, C.; Yanai, R.; Whitbeck, J. Building roots in a changing environment: Implications for root longevity. New Phytol. 2000, 147, 33–42. [Google Scholar] [CrossRef] [Green Version]
  101. Fujiki, D.; Kikuzawa, K. Stem turnover strategy of multiple-stemmed woody plants. Ecol. Res. 2006, 21, 380–386. [Google Scholar] [CrossRef]
  102. Verbeeck, H.; Bauters, M.; Jackson, T.; Shenkin, A.; Disney, M.; Calders, K. Time for a plant structural economics spectrum. Front. For. Glob. Chang. 2019, 2, 43. [Google Scholar] [CrossRef] [Green Version]
  103. Ohtsuka, T.; Akiyama, T.; Hashimoto, Y.; Inatomi, M.; Sakai, T.; Jia, S.; Mo, W.; Tsuda, S.; Koizumi, H. Biometric based estimates of net primary production (NPP) in a cool-temperate deciduous forest stand beneath a flux tower. Agric. For. Meteorol. 2005, 134, 27–38. [Google Scholar] [CrossRef]
  104. Ameztegui, A.; Paquette, A.; Shipley, B.; Heym, M.; Messier, C.; Gravel, D. Shade tolerance and the functional trait: Demography relationship in temperate and boreal forests. Funct. Ecol. 2017, 31, 821–830. [Google Scholar] [CrossRef]
  105. Ishii, H.; Ford, E.D. The role of epicormic shoot production in maintaining foliage in old Pseudotsuga menziesii (Douglas-fir) trees. Can. J. Bot. 2001, 79, 251–264. [Google Scholar]
  106. Sumida, A.; Terazawa, I.; Togashi, A.; Komiyama, A. Spatial arrangement of branches in relation to slope and neighbourhood competition. Ann. Bot. 2002, 89, 301–310. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Guillemot, J.; Kunz, M.; Schnabel, F.; Fichtner, A.; Madsen, C.P.; Gebauer, T.; Härdtle, W.; von Oheimb, G.; Potvin, C. Neighbourhood-mediated shifts in tree biomass allocation drive overyielding in tropical species mixtures. New Phytol. 2020, 228, 1256–1268. [Google Scholar] [CrossRef] [PubMed]
Forests 11 01301 g001a 550Forests 11 01301 g001b 550
Figure 1. Young shoots of (a) Alnus sieboldiana, (b) Castanea crenata, (c) Betula ermanii, (d) Acer distylum, and (e) Fagus crenata (photographs captured in April 2002 by Hiroyuki Shirakawa).
Figure 1. Young shoots of (a) Alnus sieboldiana, (b) Castanea crenata, (c) Betula ermanii, (d) Acer distylum, and (e) Fagus crenata (photographs captured in April 2002 by Hiroyuki Shirakawa).
Forests 11 01301 g001aForests 11 01301 g001b
Forests 11 01301 g002 550
Figure 2. A schematic diagram demonstrating the measurement of light environments of different shoots in this study. A sensor was fixed horizontally with a hand-held measuring bar. (Illustration by Kohei Koyama).
Figure 2. A schematic diagram demonstrating the measurement of light environments of different shoots in this study. A sensor was fixed horizontally with a hand-held measuring bar. (Illustration by Kohei Koyama).
Forests 11 01301 g002
Forests 11 01301 g003 550
Figure 3. Histogram of shoot elongation period. The shoot elongation period is defined as the time interval between the emergence of the first and last leaves.
Figure 3. Histogram of shoot elongation period. The shoot elongation period is defined as the time interval between the emergence of the first and last leaves.
Forests 11 01301 g003
Forests 11 01301 g004 550
Figure 4. Relative photosynthetic photon flux density (rPPFD) at the shoot tips in summer and autumn in 2002 and number of surviving or dead shoots in April 2003. Dates of rPPFD measurements in 2002 are as follows: A. sieboldiana, 15–24 June; C. crenata, 19 July; B. ermanii, 17 June; A. distylum, 4 August–1 September; F. crenata, 27 August–26 September.
Figure 4. Relative photosynthetic photon flux density (rPPFD) at the shoot tips in summer and autumn in 2002 and number of surviving or dead shoots in April 2003. Dates of rPPFD measurements in 2002 are as follows: A. sieboldiana, 15–24 June; C. crenata, 19 July; B. ermanii, 17 June; A. distylum, 4 August–1 September; F. crenata, 27 August–26 September.
Forests 11 01301 g004
Forests 11 01301 g005a 550Forests 11 01301 g005b 550
Figure 5. Comparison of the light environments between the first (early stage of the growth period, black curves) and the second (late stage, red curves) measurements illustrated by empirical cumulative distribution functions. (a) All the shoots were analyzed for both stages. (b) All the shoots were analyzed for the early stage, and only the shoots that survived until April 2003 were analyzed for the late stage. p-values are the results of two-sample Kolmogorov–Smirnov tests, which were used to determine whether the difference between each pair of distributions (early vs. late) was significant. N.S., not significant.
Figure 5. Comparison of the light environments between the first (early stage of the growth period, black curves) and the second (late stage, red curves) measurements illustrated by empirical cumulative distribution functions. (a) All the shoots were analyzed for both stages. (b) All the shoots were analyzed for the early stage, and only the shoots that survived until April 2003 were analyzed for the late stage. p-values are the results of two-sample Kolmogorov–Smirnov tests, which were used to determine whether the difference between each pair of distributions (early vs. late) was significant. N.S., not significant.
Forests 11 01301 g005aForests 11 01301 g005b
Forests 11 01301 g006 550
Figure 6. Shoot survival rates in relation to wood density. Fc: Fagus crenata (data from [24]); the original publication contained a typographical error (the same numbers were expressed in the wrong units [kg m−3], and therefore the correct units [g cm−3] have been used here, based on personal communication with the first author of [24]. Ad: Acer distylum (calculated from values in [103]), Be: Betula ermanii (data from [104]), and Cc: Castanea crenata (data from [24]). The wood density of A. sieboldiana was not found in our literature survey.
Figure 6. Shoot survival rates in relation to wood density. Fc: Fagus crenata (data from [24]); the original publication contained a typographical error (the same numbers were expressed in the wrong units [kg m−3], and therefore the correct units [g cm−3] have been used here, based on personal communication with the first author of [24]. Ad: Acer distylum (calculated from values in [103]), Be: Betula ermanii (data from [104]), and Cc: Castanea crenata (data from [24]). The wood density of A. sieboldiana was not found in our literature survey.
Forests 11 01301 g006
Table
Table 1. Sample sizes of current-year shoots from the saplings of different species of trees.
Table 1. Sample sizes of current-year shoots from the saplings of different species of trees.
Alnus sieboldianaCastanea crenataBetula ermaniiAcer distylumFagus crenata
No. of saplings33364
Tree height (m)0.81–1.030.95–1.130.61–0.810.86–1.720.81–1.02
No. of shoots for the shoot census 1581425318325343
No. of shoots for the leaf census 1189251259288339
1 Total number of current-year shoots from all saplings of each species that we investigated.
Table
Table 2. Growth and survival rates of the shoots.
Table 2. Growth and survival rates of the shoots.
A. sieboldianaC. crenataB. ermaniiA. distylumF. crenata
Species categoryFastFastMediumSlowSlow
Annual shoot elongation 1,2 (cm year−1)26.98.04.41.52.2
Shoot elongation period (days) 1156331883.5
Number of daughter buds per shoot 1,2,35.12.71.91.11.1
Shoot survival rate0.540.780.880.930.98
1 Mean values for each species. 2 The lengths of sylleptic shoots and the number of their vegetative buds were summed with those of their parent proleptic shoots. 3 Shoots without vegetative buds were not included.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Koyama, K.; Shirakawa, H.; Kikuzawa, K. Redeployment of Shoots into Better-Lit Positions within the Crowns of Saplings of Five Species with Different Growth Patterns. Forests 2020, 11, 1301. https://doi.org/10.3390/f11121301

AMA Style

Koyama K, Shirakawa H, Kikuzawa K. Redeployment of Shoots into Better-Lit Positions within the Crowns of Saplings of Five Species with Different Growth Patterns. Forests. 2020; 11(12):1301. https://doi.org/10.3390/f11121301

Chicago/Turabian Style

Koyama, Kohei, Hiroyuki Shirakawa, and Kihachiro Kikuzawa. 2020. "Redeployment of Shoots into Better-Lit Positions within the Crowns of Saplings of Five Species with Different Growth Patterns" Forests 11, no. 12: 1301. https://doi.org/10.3390/f11121301

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop