Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees

Photosynthetic characteristics of tall trees play important roles in improving the accuracy of ecosystem models, but they are laborious to be accurately measured or estimated owing to the influence of multiple factors. To clarify the main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees, we collected data on the photosynthetic, morphological, and meteorological characteristics by a long-term observation through the whole growing season. Vertical and seasonal patterns of leaf photosynthetic characteristics and their impact factors were analyzed. Results showed that maximum net CO 2 assimilation ( A max ), light saturated stomatal conductance ( g s-sat ), respiration rate ( R D ), needle mass per area (NMA), and ratio of needle length to needle width ( r lw ) all significantly and negatively correlated with relative depth into crown (RDINC), that was caused by the adaptive alteration of mesophyll tissue to the differed light intensity and humidity. A max and g s-sat both showed a similar “parabolic” seasonal trend, that was not only affected by the variation of environment but also the leaf economic spectrum, such as NMA. Our results suggested that spatiotemporal variations of crown photosynthetic characteristics were directly influenced by leaf economic spectrum but fundamentally affected by the long-term acclimation to surrounding environmental factors. This is helpful to optimize the crown photosynthesis model to assess instantaneous or even long-term photosynthetic production, in order to clarify the balance of supply and demand within crown, and further guide the effective pruning for individual tree.


Introduction
Photosynthetic characteristics are indicative of physiological parameters that correlate with forest primary production [1] and drive carbon uptake [2] .They directly participate in natural carbon cycle but are sensitive to environmental conditions; they are often used to represent the resistance and resilience of vegetation to extreme climates [3] , pests and diseases [4−5] , fires [6] , and toxic metals [7,8] .Convergent emergence or loss of photosynthetic phenotypes may facilitate adaptation to ecologically similar environments [9] .
In forestry, differences in photosynthetic characteristics among different tree species are prominent [10] .The photosynthetic characteristics of leaves within the same species often have significant spatial heterogeneity owing to the complex spatial structure of the crown [11] , which is particularly evident in the vertical structure of the crown [12,13] .Light [14,15] and water potential [16] are thought to be the key determining factors of vertical variation patterns in tree crown photosynthetic traits.However, the dominant roles of two species could shift under different forest densities and tree sizes.Generally, in closed canopies, light is the major factor that leads to the spatial heterogeneity of leaf photosynthetic traits [17] , cause leaf will alter its structure and physiological function to adapt to the lighting environment.Likewise the differences appears between shade and sun leaves within the crown [13] .Sun leaves have a high maximum net CO 2 assimilation (A max ), respiration rate (R D ), and light compensation point (LCP) but lower light utilization efficiency (LUE) and light saturation point (LSP) [18] .Water potential tends to be the dominant factor for huge dominant trees [19] , cause it represents the ability to transport the water from the root to crown.Crown photosynthetic characteristics also show seasonal variation with individual development and environmental changes [2,21] .For example, A max usually shows a parabolic seasonal variation patternis due to its positive correlation with the temperature (T), solar radiation, and soil moisture [23−25] .However, R D exhibits an opposite 'U'shaped trend [18] due to the decline of cytochrome-mediated respiration [26] and temperature sensitivity [27] .
Studies have shown that spatial and seasonal variations in canopy photosynthetic characteristics are closely associated with the comprehensive impact of light [28] , temperature [25,29] , humidity [30] , and seasonal patterns of leaf structural traits [31,32] .Generally, adequate light and suitable temperature and humidity can promote the photosynthesis, by improving light energy utilization [33] and photosynthetic enzyme activity [34] , however, some environmental conditions will inhibit the photosynthesis [35] .For example, the common natural phenomena "midday depressions" was a self protection mechanism by regulating blade osmotic pressure and maintaining mesophyll cell activity, in respond to the stress of strong light, high temperature and low humidity [36] .Additionally, the age [23,37,38] and sex of dioecious tree species [39] influence the photosynthetic characteristics of leaves to some extent due to the difference of mesophyll cell structure.When simulating the mechanism process model of canopy, forest productivity, and carbon

A c c e p t e d & U n -e d i t e d
absorption processes, the spatial and seasonal variations in canopy leaf photosynthetic characteristics need to be considered simultaneously, otherwise, incorrect results will be obtained [16] .Therefore, the spatiotemporal heterogeneity and driving mechanisms of canopy photosynthetic characteristics need to be urgently clarified.
This study used the artificial forest of the main afforestation species (Larix olgensis) in the northern area as the research object that was tracked and monitored throughout its growing season.The factors of photosynthetic characteristics, leaf morphology, crown structure and environmental factor were collected.The overarching aims of this study are twofold: first, is there a significant difference of photosynthetic characteristics in different crown positions, growing periods and trees individuals.Secondly, what is the variation patterns of photosynthetic characteristics within the tree crown during growing season.Finally, provide a comprehensive assessment of the relationships between photosynthetic characteristics and crown structure, leaf morphology and environmental conditions.

Site description
The experiments were conducted in 2017 at the experimental forest farm of Northeast Forestry University in Maoershan, Haerbin, China (Northern latitude: 45°2′20″~45°18′16″, East longitude: 127°18′0″~127°41′6″; altitude 400 m above sea level).The climate in the Maer Mountain region belongs to the temperate continental monsoon climate, with an average annual temperature of 2.4 °C, the highest temperature being 34°C and the lowest being −40 °C, approximately 125 days of frost-free period, an average annual precipitation of 700mm, and dark brown soil as the main type.Total forest coverage is approximately 83.3%, including 14.7% plantation.

Sample selection
Five fixed plots of 20m×30m with same site quality in the young L. olgensis plantation are set up, and the trees with the diameter at breast height (DBH) larger than 5cm in each plot are measured.The specific measurement factors include tree height (H), DBH, crown width (CW) and the relative coordinates (x i , y i ) of each tree are investigated.Then the average DBH of five plots are calculated according to the per tree measurement data.Build a scaffold around the sample tree, ensuring that the sample tree is completely surrounded in it, ensuring that all branches of the sample wood and each position of each branch can be measured on the scaffold, each layer is connected by a tread.After each measurement, the upper tread needs to be removed to avoid the influence on the measurement result caused by the blocking of light.The crown length of each sample tree is divided into several vertical sections based on the whorls from tree top to bottom and started numbering from V1st.Three healthy and fully expanded needle located within each section in the middle of the foliated branches in sunny, semisunny and shaded crowns were selected, according to the sample selection principles based on Qiang [40] .The photosynthetic characteristics in each vertical section was the average values of measurements taken from different directions (sunny, semisunny and shaded crown).

Photosynthetic gas exchange measurements
The photosynthetic light response (PLR) curves were measured twice per month (the beginning of a month and midmonth) during the growing season (from approximately May 15th to September 10th).All photosynthetic properties were measured with a portable steady-state photosynthesis system (LI-6400XT, LI-COR, Inc., Lincoln, NE, USA) equipped with a standard LED light source (6400-02B, LI-COR, Inc., Lincoln, NE, USA).Sample chamber is acclimated for 20 min at a CO 2 concentration of 390 ppm with a CO 2 mixer (6400-01, LI-COR, Inc., Logan, NE, USA) to maintain a stable CO 2 supply.All sample cluster needles are acclimated under a PAR of 1400 µmol m −2 s −1 for 10 to 20 min by the LED light source (6400-02B, LI-COR, Inc., Lincoln, NE, USA).PLR curve is measured at 10 PAR gradients: 2000, 1500, 1200, 1000, 500, 200, 150, 100, 50, 0 µmol m −2 s −1 .Sample cluster needles were allowed to equilibrate at a minimum of 2 min at each measurement before data logged, and a calibration (match) was performed after each count.At the same time, the temperature of the leaf (Air temperature, Tair), the relative humidity (Relative humidity, RH) and the vapor pressure deficit (Vapor pressure deficit, VPD) are recorded.After the measurement, the depth into the crown (Depth into crown, DINC) of each measured sample was recorded in the crown, and the relative depth into the crown (Relative depth into crown, RDINC) is calculated according to the crown length (Crown length, CL): RDINC = DINC / CL.

Needle morphology measurements
Once the photosynthetic gas exchange measurements were completed, the sample cluster needles were immediately taken back to the laboratory for measuring the needles mass per area (NMA, g m −2 ).Each cluster sample was scanned immediately after collection and then surveyed with an image analysis softwar (Image-Pro Plus 6.0, Media Cybernetics, Inc., Bethesda, USA), resulting in the projected needle area (NA, m 2 ), needle length (l) and needle width (w), and consequently obtain the ratio of needle length to needle width (rlw).Then, the scanned samples were dried to a constant weight at 85 °C and weighed to dry weight (WD).The NMA was calculated: NMA = WD / LA.

Photosynthetic parameters
The light-saturated CO 2 assimilation (A max , µmol m −2 s −1 ) and dark respiration (R D , µmol m −2 s −1 ) were estimated from the PLR curves using the modified Mitscherlich model [41] : where A n is the net CO 2 assimilation (µmol m −2 s −1 ), A max is the light-saturated net CO 2 assimilation (µmol m −2 s −1 ), α is the apparent quantum yield, PAR is the photosynthetically active radiation (µmol m −2 s −1 ), and R D is the dark respiration rate (µmol m −2 s −1 ).The light saturated stomatal conductance (g s-sat , mol m −2 s −1 ) was determined as the corresponding gs value of A max .Wateruse efficiency (WUE sat , mmol CO 2 mol H 2 O −1 ) was calculated as the ratio of A max to gs-sat.As the environment conditions were not maintained under a certain value during the measurement of PLR curves except CO 2 concentration (stabilized at 390 ppm).

Statistical analysis
Data summary refers to Table 1.Statistical analyses were performed using the R software 4.2.2 [42] .A three-way repeatedmeasures analysis of variance (ANOVA) was performed on all experimental variables to evaluate the effects of individual tree (T), period (P), and crown layer (L) on light-saturated CO 2 assim-

A c c e p t e d & U n -e d i t e d
ilation (A max ), dark respiration (R D ), light-saturated stomatal conductance (g s-sat ), and water-use efficiency (WUE sat ).Pearson's correlation analysis was used to test the relationships among all the measured variables.The significance of all the statistical analyses was at α = 0.05 level.All figures were drawn using the ggplot2 package in R software 4.2.2.

Vertical profiles of photosynthetic and morphological parameters of needles
Photosynthetic parameters (A max , R D , g s-sat and WUE sat ) and morphological parameters (LMA and r lw ) differed significantly among the different measurement phases, individual trees, and vertical locations of the crown (Table 2).Considering the average pattern across the five sample trees, nearly all physiological and morphological parameters of the needles exhibited a similar vertical profile, which decreased noticeably with increasing RDINC (Fig. 1).However, the mean WUE sat of the five sampled trees followed the opposite trend (Fig. 1f).A max significantly decreased with RDINC (Fig. 1a), and the mean A max in the top crown (12.84 µmol m −2 s −1 ) was almost 2.7 times higher than that in the bottom crown (4.81 µmol m −2 s −1 ).Although there were significant tree-specific differences in g s-sat , NMA, and R D (Table 2), their tendencies demonstrated a pronounced decrease with increasing RDIINC (Fig. 1b, 1c, and 1d).The mean values of R D , g s-sat , and NMA varied by 2.5-fold, 3.3-fold, and 2.3fold, respectively, from the top to the bottom of the crown.Mean r lw exhibited a slight decrease with RDINC when RDINC was lower than 0.3 (upper crown) (Fig. 1e) but then sharply

Table 2.
Results of the three-way repeated measures ANOVA of photosynthetic and morphological parameters.decreased when RDINC was greater than 0.4 (middle and lower crown).In contrast, mean WUE sat showed a weak upward trend with increasing RDINC (Fig. 1f), varying by only 0.015 mmol CO 2 mol H 2 O −1 from top to bottom.As mentioned above, photosynthetic and morphological parameters were significantly affected by the vertical location of the crown.However, it is unknown whether the same pattern remains during the entire growth period.Analysis of variance was performed on photosynthetic and morphological parameters based on the vertical locations (upper, middle, and lower crown) in each measurement phase, and the results are summarized in Table 3. r lw was the only parameter that showed a significant vertical difference across the entire growth season (upper > middle > lower crown).A max , g s-sat , and NMA demonstrated a similar vertical pattern in June, but the mean values of A max and g s-sat were not significantly different between the upper and middle crown during the early period of needle expansion (PI, May).Mean NMA showed no significant vertical difference within the crown.R D showed no significant difference with respect to vertical location in the crown during the early period of needle expansion but showed a significantly greater values in the upper crown than in the middle and lower crowns after June.WUE sat only showed a slight vertical difference at PVI, PVII, and PIV.

A c c e p t e d & U n -e d i t e d
All photosynthetic and morphological parameters differed significantly among the individual sample trees and fluctuated during the growing seasons (Fig. 2).Mean daily A max increased with time until late summer (at early August) to a maximum of nearly 9.42 µmol m −2 s −1 (Fig. 2a).For the remaining season, mean A max ranged from 8.25 µmol m −2 s −1 to 8.34 µmol m −2 s −1 .The mean daily R D exhibited a significant decrease over time in early summer to a minimum of near 0.77 µmol m −2 s −1 and was then restored to 1.04 µmol m −2 s −1 (Fig. 2d).g s-sat and NMA exhibited a similar time course with an increase during the growing season (Fig. 2b and 2c), but an abnormal peak appeared in early August.Mean WUE sat demonstrated a time course that is opposite to that of g s-sat and NMA (Fig. 2f).Mean NMA increased abruptly at the early period of needle expansion (PI, May), then remained stable until the second half of August (PVII), but finally increased at the end of growth.

Correlations between photosynthetic and meteorological parameters
The relationships between photosynthetic and main meteorological parameters are shown in Fig. 3.A max significantly correlated to T leaf , RH, and VPD in the entire treatment (Fig. 3a,  3c, and 3c), in which A max positively correlated to T leaf and RH but negatively correlated to VPD.The correlation was stronger between A max and RH (r = 0.46) than between A max versus T leaf (r = 0.27) and VPD (−0.28).R D positively and linearly correlated with VPD (r = 0.31, Fig. 3i), but a stronger nonlinear relationship was observed between R D and T leaf (r = 0.61, Fig. 3g).RH poorly correlated with R D (r = 0.07, Fig. 3h).g s-sat significantly and nonlinearly correlated with RH (positive) and VPD (negative) (Fig. 3e and 3f).In contrast, WUE sat negatively correlated with RH (r = −0.65,Fig. 3k), positively correlated with VPD (r = 0.64, Fig. 3l), and weakly correlated with T leaf (r = 0.3, Fig. 3j).

Relationships between physiological parameters and LMA
A max and R D exhibited a positive and significant correlation with LMA (Fig. 4a and 4c), but there were slight differences in the correlation coefficients among the individual sample trees.Although g s-sat showed a similar correlation with LMA as A max and R D , the correlation was weaker (r = 0.35, Fig. 4b).WUE sat only significantly correlated with LMA for two sample trees, even although a significantly negative relationship was observed for all the sample trees (Fig. 4d).WUE sat negatively correlated with LMA but was more significant seasonally than spatially.

Spatial variation of needle physiology and morphology
Previous studies have suggested that light [15] and water potential [19] are the most important factors affecting the vertical pattern of leaf physiology and morphology, but the primary driver between these two factors is still being debated [44] .Recently, it has become increasingly accepted that the effects of these two factors vary with tree height [45] .Light reportedly affects leaf functions and structures in short trees [15,19] , but for tall trees, a decrease in water potential considerably limits their leaf expansion and photosynthetic rate [46] .Our results showed that A max and g s-sat decreased significantly from crown top to bottom (Fig. 1a and 1c), which is consistent with that of other studies [48] .Martin

A c c e p t e d & U n -e d i t e d
under less irradiance had lower leaf stomatal conductance than sun leaves [18] .R D negatively correlated with RDINC (Fig. 1b), as previously documented for different species [47] , because leaves generally adapted to dark environments by reducing R D and Non-photochemical quenching.High leaf tissue density [15] decreased mesophyll conductivity to gas, and restrained R D [48] , which was also proved by the higher positive correlation between R D and NMA (Fig. 4c).The vertical pattern of R D partly decreased A max with increasing tree height but was not significant.NMA is one of the main morphological traits that changes in response to light variations [49] ; thus, in our study, NMA followed the same pattern as A max , g s-sat , and R D (Fig. 1e), suggesting that needles synthesize more photosynthetic tissue with increasing height to maximally use sufficient illumination.
Studies revealed that the universal NMA gradient within the tree crowns or forest canopies is likely driven by solute content, leaf thickness [50] , leaf turgor pressure [47] , and leaf tissue density [15] , which reflect the plasticity and adaptability of foliage to the environment.Physiological variations in needles are usually accompanied by a corresponding change in their external form [19] .Our results showed that r lw significantly decreased with increasing RDINC (Fig. 1f), which further implied that trees maximized their photosynthetic efficiency by adjusting their foliage morphology to adapt to different environments in the vertical direction.Variations in WUE sat originate from variations in photosynthetic rate, stomatal conductance, or both [51] .Studies showed that WUE sat negatively correlated with g s-sat [52] .In this study, WUE sat showed an opposite vertical tendency to g s-sat (Fig. 1d), which increased slightly and positively with RDINC, further supporting the opinion that foliage in the lower crown or canopy usually compensates for low resources by improving the utilization efficiency of site resources [53] .

Seasonal variation of needle physiology and morphology
Understanding the effect of seasonal variations on physiological and morphological parameters is critical for accurate modeling of carbon dioxide uptake by ecosystems, which can then be used to determine the magnitude of ecosystem carbon fluxes [54] .Neglect of this variation may results in incorrect simulations of carbon uptake [55] .Previous studies revealed that A max and g s-sat generally show a trend similar to a typical parabolic curve during growing season [23,56] although contrary results have been reported [57] .However, A max strongly correlated with g s-sat in the above investigations, indicating that stomatal behavior has a pronounced impact on A max .Our results show that all the physiological and morphological parameters fluctuated during the growth season.A max had a parabolic seasonal variation, similar to that reported in other studies [23] though accompanied by slight fluctuations in different trees (Fig. 2a), which was probably caused by the high correlation between A max and seasonal variation of environment conditions (Fig. 3) [40] .Kunert et al. confirmed that short-term exposure to high temperatures poses a considerable threat to conifer species in Central European forest production systems [58] .During spring, an increase in A max resulted from a gradual increase in photosynthetic capacity [39] .A decrease in A max during needle senescence is associated with a decrease in mesophyll conductivity to carbon dioxide owing to the increasing size of chloroplasts, starch grains, plastoglobuli, and the resorption of nitrogen [59] .Seasonal variations in leaf photosynthetic traits, including  maximum photosynthesis rate, maximum carboxylation rate, and mesophyll and stomatal conductance, can be well explained based on photoperiod variations [2] .In addition, under both winter and drought stress, the main challenge for plants is that electron acceptor regeneration processes markedly slow down compared to primary photosynthetic processes, and this creates an imbalance between absorption and utilization of light energy [30] .Previous studies have suggested that seasonal variations in R D are mainly driven by seasonal patterns in temperature and NMA [60] .It is well known that starch and soluble sugars are the main reactants in respiratory process; thus, their content directly affects respiration.Temperature also indirectly limits respiration by affecting the activity of enzymes that participate in respiration [61] .Report showed that a reduction in leaf expansion phase was due to a decrease in cytochrome-mediated respiration [21] .Our results implied that R D was significantly correlated with T leaf (Fig. 3b) and NMA (Fig. 4b).The seasonal pattern of R D showed an obvious reduction in leaf expansion phase and then slightly recovered with a little fluctuation (Fig. 2b), following a similar trend in other studies [62] .NMA showed a progressive increase throughout the growing season (Fig. 2e) owing to the accumulation of structural proteins and calcium.Seasonal variation in WUE sat was different from that in g s-sat (Fig. 2d), probably because WUE sat and g s-sat negatively correlated [51] .Previous studies on seasonal patterns of leaf length, width, and thickness have shown that they universally follow a saturation or parabolic curve [60] throughout the growth season, reflecting the dynamic nature of photosynthetic acclimation [63] .We also observed variations in leaf shape and found that the ratio of length to width (r lw ) showed a saturated tendency (Fig. 2f).The increase at the end of the growing season indicated that the needles started to senescence.
A further analysis of seasonal difference in photosynthetic rates among different canopy positions was conducted (Table 4), and the result indicated that A max , WUE sat and g s-sat were significantly differed in upper crown but not significant in lower crown.Conversely, R D showed significantly seasonal difference in lower crown but not significant in upper crown.Rare studys mentioned relevant results, but some researches have proved that photosynthesis was more sensitive to light intensity and respiration was mainly affected by temperature [51−53] .In our study, the closed-canopy caused an obvious seasonal change of light intensity in upper crown, but weak in lower crown.However, the seasonal variation of temperature was evident in the whole crown.Thus, A max , WUE sat and g s-sat showed different seasonal difference compared to R D .

Relationship between needle physiology and environment
T leaf showed a significant parabolic correlation with A max (Fig. 3a), corroborating the results of many other studies that focused on different tree species such as, Quercus crispul [59] , Picea mariana [64] , Pinus cembra (Wieser 2010) [65] .Both RH and VPD showed a significant relationship with A max (Fig. 3e and 3i), particularly in the upper crown, presumably because needles in the upper crown are exposed to environmental stresses more frequently, and the variations in RH and VPD in the upper crown are more sensitive and greater [66] than that in other crowns.R D exhibited a typical exponential relationship with T leaf (Fig. 3b), corroborating the results of other studies [48,60,64] .Both g s-sat and WUE sat significantly correlated with RH (Fig. 3g and  3h) and VPD (Fig. 3k and 3l), but the tendencies were diametrically opposite.Similar processes have been observed in other studies [39,52] .Some studies have suggested that variations in WUE sat across dates are primarily driven by g s-sat [51] and our investigation showed that WUE sat significantly correlated to g s-sat (r = −0.71).Thus, we suggest that the relationship among WUE sat , versus RH and VPD is likely caused by the influence of RH and VPD on g s-sat .

Relationship between needle physiology and NMA
NMA plays an important role in predicting foliar physiological function, serves as a parameter in ecosystem modeling, and is used as an indicator for potential growth rate [67] .Our results showed that A max and g s-sat both had a significant positive correlation with NMA (Fig. 4a and 4c), similar to the results of previous studies on other species [68] .Other studies have shown a negative relationship between mass-based A max and g s-sat versus NMA, probably because of the vertical pattern of NMA [46] .Han found a negative relationship between A max and g s-sat versus NMA for Pinus densiflora, probably because of a higher NMA value (>200 g m -2 ) than that in our study (<130 g m −2 ) [69] .Moreover, a NMA value of up to 500 g m −2 for Pinus monticola (Marshall 2003)  [70] , 800 g m −2 for Sequoia sempervirens (Koch 2004) [19] , and 1000 g m −2 for Pseudotsuga menziesii and Pinus ponderosa [70] ; almost all these species are categorized as tall tree species with negative relationship between A max and NMA.Therefore, we speculate that the relatively low NMA in our study may not be sufficient to limit mesophyll conductivity to carbon dioxide, and consequently, A max and R D is significantly and positively correlated with NMA, probably because of starch and soluble sugar contents [60] .However, WUE sat had a stronger correlation with RH and VPD than with NMA (Fig. 3h, 3l, and 4d), indicating that variations in WUE sat across the growing season were primarily driven by the environment rather than by the needle morphology in this study.

Conclusions
Our study found that the spatial and seasonal variations of crown photosynthetic parameters for Larix olgensis were directly influenced by NMA, RH and VPD, in which NMA generally reflected the adaptability of leaves to the environmental factors.Thus, clarifying the response relationships between micro-environment and thinning intensity will contribute to

Fig. 4
Fig. 4 Relationships between (a) light-saturated net photosynthetic rate (Amax) and needle mass per area (NMA); (b) light-saturated stomatal conductance (gs-sat) and NMA; (c) dark respiration (RD) and NMA; (d) light-saturated water use efficiency (WUEsat) and NMA.R values are the Pearson correlation coefficients.Solid lines represent the fitting result and are based on linear equations.

A c c e
p t e d & U n -e d i t e d

Table 1 .
Sample tree and data summary.752 photosynthetic light response curves were investigated, including 9303 instantaneous environmental and functional factors, from 36 pseudowhorls from five planted Larix olgensis trees.LMA, leaf mass per area, Tair, air temperature, VPD, vapor pressure deficit, PAR, photosynthetically active radiation, RDINC, relative depth into the crown (RDINC).No., Mean, Std., Max. and Min. are the numbers, mean value, standard deviation, maximum value and minimum value, respectively.

Table 4 .
Results of the One-way ANOVA of photosynthetic parameters in each vertical layer.tionalPlant Biology 35:439−447 Wyka TP, Żytkowiak R and Oleksyn J. 2016.Seasonal dynamics of nitrogen level and gas exchange in different cohorts of Scots pine needles: a conflict between nitrogen mobilization and photosynthesis?European Journal of Forest Research 135:483−493 23.Yin HM, Yang MH, Li PL, Yu XL, Xiong H, et al. 2022.Seasonality of Photosynthetic Physiology and Leaf Anatomy in Three Different Quercus L. Section Cyclobalanopsis Seedlings of Quercus chungii, Quercus gilva, and Quercus glauca in the Subtropical Region of South China.Forests 13:206724.Shimada R and Takahashi K. 2022.Diurnal and seasonal variations in photosynthetic rates of dwarf pine;Pinus pumila;at the treeline in central Japan.Arctic, Antarctic, Alpine Res 54:1−12 25.Collier DE and Thibodeau BA. 1995.Changes in respiration and chemical content during autumnal senescence of Populus tremuloides and Quercus rubra leaves.Tree Physiology 15:759−764 26.Atkin OK and Bonal D. 2015.Global variability in leaf respiration in relation to climate and leaf traits.New phytol 206:614−636 27.Coble AP and Cavaleri MA. 2015.Light acclimation optimizes leaf functional traits despite height-related constraints in a canopy shading experiment.Oecologia 177:1131−1143 28.Xu MJ, Wang QY, Yang FT, Zhang T, Zhu XJ, et al. 2022.The responses of photosynthetic light response parameters to temperature among different seasons in a coniferous plantation of subtropical China.Ecological Indicators 145:109595 29.Ilya EZ, Alexander VK, Pavel PP, Yury VI, Vladimir DK, et al. 2019.Comparative photosynthetic responses of Norway spruce and Scots pine seedlings to prolonged water deficiency.Journal of Photochemistry and Photobiology B-Biology 201:111−659 30.Coble AP and Cavaleri MA. 2017.Vertical leaf mass per area gradient of mature sugar maple reffects both height-driven increases in vascular tissue and light-driven increases in palisade layer thickness.Tree Physiology 37:1337−1351 31.Xiong DL and Flexas J. 2021.Leaf anatomical characteristics are less important than leaf chemical properties in determining photosynthesis responses to top-dress N. Journal of Experimental Botany 72:5709−5720 32.Li W, Li J, Wei J, Niu C, Yang D, Jiang, B. 2023.Response of photosynthesis, the xanthophyll cycle, and wax in Japanese yew (Taxus cuspidata L.) seedlings and saplings under high light conditions.Mayoral C, Calama R, Sánchez-González M and Pardos M. 2015.Modelling the influence of light, water and temperature on photosynthesis in young trees of mixed Mediterranean forests.New Forests 46:485−506 The different parameters have been identified and described in the text.*, 0.01 < P ≤ 0.05; **, 0.001 < P ≤ 0.01; ***, P ≤ 0.001.