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2024 Volume 4
Article Contents
Abstract
Introduction
Materials and methods
Plant materials and growth conditions
Experimental treatments and design
Measurements
Soil water content and plant growth and physiological traits
Hyperspectral imaging of phenotypic responses to drought stress
Multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of physiological response to drought stress
Statistical analysis
Results
Changes in soil water content, leaf relative water content, and visual turf quality during drought stress
Hyperspectral and multispectral imaging of phenotypic responses to drought stress
Chlorophyll fluorescence imaging of photosynthetic responses to drought stress
Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress
Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence
Discussion
Conclusions
Acknowledgments
Conflict of interest
Rights and permissions
References
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ARTICLE   Open Access    

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

  • 1.

    School of Forestry, Hebei Agricultural University, Baoding 071000, PR China

  • 2.

    School of Forestry, Northeast Forestry University, Harbin 150040, PR China

  • 3.

    School of Forestry, Beihua University, Jilin 132000, PR China

More Information
  • Received: 05 September 2023
    Revised: 15 November 2023
    Accepted: 22 November 2023
    Published online: 09 January 2024
    Forestry Research  4,  Article number: e001 (2024)  |  Cite this article
  • 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 throughout the entire growing season. Vertical and seasonal patterns of leaf photosynthetic characteristics and their impact factors were analyzed. Results showed that maximum net CO2 assimilation (Amax), light saturated stomatal conductance (gs-sat), respiration rate (RD), needle mass per area (NMA), and ratio of needle length to needle width (rlw) 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. Amax and gs-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 trees.

  • Introduction

    Drought is a major abiotic stress affecting plant growth which becomes even more intensified as water availability for irrigation is limited with current climate changes[1]. Timely detection and identification of drought symptoms are critically important to develop efficient and water-saving irrigation programs and drought-tolerance turfgrasses. However, turfgrass assessments of stress damages have been mainly using the visual rating of turf quality which is subjective in nature and inclined to individual differences in light perception that drives inconsistency in estimating color, texture, and pattern of stress symptoms in grass species[2−4]. Remote sensing with appropriate imaging technology provides an objective, consistent, and rapid method of detecting and monitoring drought stress in large-scale turfgrass areas, which can be useful for developing precision irrigation programs and high-throughput phenotyping of drought-tolerance species and cultivars in breeding selection[5].

    Spectral reflectance and chlorophyll fluorescence imaging are emerging tools for rapid and non-destructive monitoring of drought effects in crops. These tools combine imaging and spectroscopy modalities to rigorously dissect the structural and physiological status of plants[6,7]. Spectral reflectance imaging captures reflected light (one out of three fates of light: reflect, absorb and transmit when striking leaf) at different wavelengths ranging from visible to near-infrared regions to characterize vegetation traits[8,9]. Within spectral reflectance imaging, multispectral imaging on one hand measures reflected light in three to ten different broad spectral bands in individual pixels[10,11]. Hyperspectral imaging on the other hand captures reflected light in narrow and more than 200 contiguous spectral bands. Some absorbed light by leaf is re-radiated back in the form of fluorescence and fluorescence imaging utilizes those lights in red and far-red regions to capture plant physiological status[12]. When drought progresses, plants start to develop various symptoms (physiological modifications) gradually over time[13]. Some of those symptoms include stomata closure, impediment in gas exchange, change in pigment composition and distribution which result in wilting and associated morphological alteration in leaf color (senescence), shape (leaf curling) and overall plant architecture. As different plant components or properties reflect light differently at different wavelengths and patterns of reflectance and fluorescence change along with plant stress and related symptoms development, spectral reflectance and fluorescence imaging provide accurate, reliable and detailed information for crop drought monitoring. Fluorescence imaging primarily based on fluorescing plant components or chlorophyll complex in photosynthetic antenna and reaction centers and therefore it mainly monitors stress development by tracking changes in overall photosynthetic performance or other metabolism that interfere with photosynthetic operation[9,14]. Multispectral imaging, hyperspectral imaging, or chlorophyll fluorescence has been used in different studies for plant responses to drought stress in various plant species[10,15−17]. The comparative approach of multiple imaging technologies could help to find the efficient methods for the evaluation of plant responses and tolerance to drought[18].

    Vegetation indices derived from multispectral or hyperspectral imaging and fluorescence parameters typically are ratio or linear combinations of reflectance and fluorescence emissions from leaves or canopy of plants, respectively[19,20]. Canopy reflectance at different wavelengths and chlorophyll fluorescence varies with canopy color and density and changes with environmental conditions that affect plant growth, including drought stress[14,20,21]. These variations in reflectance and fluorescence are captured by vegetation indices, such as normalized difference vegetation index (NDVI) and fluorescence parameters including the ratio of variable fluorescence to maximum fluorescence (Fv/m) which are commonly used to evaluate environmental impact on plant growth. Other indices reflect physiological health of plants, such as photochemical reflectance index (PRI) has recently been reported to be useful for drought stress assessment in crops[19]. Previous research identified varying sensitivity of PRI and NDVI to detect water stress; for example, Sun et al.[22] found PRI to be a prominent indicator of drought stress whereas Kim et al.[20] discovered NDVI had greater correlation with drought stress development. There are also several conflicting findings on the responsiveness of fluorescence parameters to drought stress. Photochemical efficiency of PSII (Fv/Fm) was found to be greatly related to drought stress by Panigada et al.[23] but Jansen et al.[24] reported Fv/Fm to be relatively insensitive to drought progression. Lu & Zhang[25] identified that coefficient of photochemical quenching (qP) was insensitive to drought stress whereas Moustakas et al.[26] reported that (qP) being the most sensitive indicator of such stress conditions. There is a need for a comprehensive study that examines multiple vegetation indices (both hyperspectral and multispectral indices) and fluorescence parameters, and parallelly assess their sensitivities to reflect plant growth and physiological status during drought stress.

    The objectives of the current study were: (1) to perform comparative analysis of drought responses of vegetation and photosynthetic indices using multispectral, hyperspectral and chlorophyll fluorescence imaging for Kentucky bluegrass (Poa pratensis L.), a cool-season perennial grass species widely used as turfgrass; (2) identify major vegetation and photosynthetic indices from the imaging technologies and correlated to visual turf quality and leaf relative water content from the destructive measurement; and (3) determine the major vegetation and photosynthetic indices that are most responsive or sensitive to the progression of drought stress that may be useful to early detection and monitoring the level of drought stress causing growth and physiological damages in cool-season grass species.

    Materials and methods

    Plant materials and growth conditions

    Sod strips of Kentucky bluegrass cultivar 'Dauntless' were collected from established field plots at the Rutgers Plant Science Research and Extension Farm, Adelphia, NJ, USA. Sods were planted in plastic pots of 18 cm diameter and 20 cm length filled with a mixture of soil (sandy loam, semi-active, mesic Typic Hapludult; pH 6.55; 260 kg·P·ha−1, 300 kg·K·ha−1) and sand in the ratio of 2/1 (v/v). Plants were established for 50-d in a greenhouse with 24/22 °C day/night average temperatures, 12-h average photoperiod and 750 μmol·m−2·s−1 average photosynthetically active radiation (PAR) with natural sunlight and supplemental lightings. Plants were well-watered, trimmed weekly to 100 mm and fertilized weekly with a 24–3.5–10 (N–P–K) fertilizer (Scotts Miracle-Gro) at the rate of 2.6 g·N·m−2 during the establishment period in the greenhouse. Once plants were well-established, they were moved to the controlled environmental growth chamber (GC72, Environmental Growth Chambers, Chagrin Falls, OH, USA). The growth chamber was controlled at 22/18 °C day/night temperature, 60% relative humidity, 12-h photoperiod and 650 μmol·m−2·s−1 PAR at the canopy level. Plants were allowed to acclimate for a week within the growth chamber conditions and then treatments were initiated.

    Experimental treatments and design

    There were two different treatments: well-watered control and drought stress. For the well-watered control, plants were irrigated once every two days with sufficient water until drainage occurred from the pot bottom or when soil water content reached the field capacity. Drought stress was imposed by withholding irrigation from each pot throughout the experiment period. Each treatment had five replicates. The experimental treatments were arranged as a complete randomized design with plants of both treatments randomly placed and relocated in the chamber twice each week to minimize effects of potential microenvironment variations in the growth chamber.

    Measurements

    Soil water content and plant growth and physiological traits

    A time-domain reflectometry system (Model 6050 × 1; Soil Moisture Equipment, Santa Barbara, CA, USA) installed with 20 cm soil moisture probe was used to measure soil volumetric water content. Volumetric water content was measured every two days in each pot to track soil moisture dynamics in control and drought stress treatments. To assess plant responses at different soil moisture levels, turfgrass quality (TQ) and leaf relative water content (RWC) were evaluated. Turfgrass quality was visually rated on a scale of 1-9 depending upon canopy color, uniformity and density[27]. A rating of 1 indicates discolored and completely dead plants, 9 indicates lush green colored healthy plants and 6 indicates the minimum acceptable turfgrass quality. Leaf RWC was measured by soaking 0.2 g fresh leaves in distilled water overnight at 4 °C[28]. Turgid leaves after overnight soaking were oven dried at 70 °C to a constant dry weight. Leaf RWC was calculated as [(fresh weight – dry weight)/ (turgid weight – dry weight)] × 100.

    Hyperspectral imaging of phenotypic responses to drought stress

    Control and drought stress pots were scanned using a close-range benchtop hyperspectral imaging system (Resonon Inc., Bozeman, MT, USA) containing Pika XC2 camera equipped with 23 mm lens. This camera took images in spectral range of 400–1,000 nm with much detailed spectral resolution of 1.9 nm in 447 different spectral channels. The camera provided 1600 spatial pixels and maximum frame rate of 165 frames per second. It had 23.1° field of view and 0.52 milli-radians instantaneous field of view. Resonon hyperspectral imaging systems are line-scan imagers (also referred to as push-broom imagers) that collect spectral data from each pixel on one line at a time. Multiple lines are imaged when an object or pot kept in scanning stage of linear stage assembly underneath the camera is moved by a stage motor. Those line images are assembled to form a complete image. The systems had regulated lights placed above the linear stage assembly to create optimal conditions for performing the scans. Lights were at the same level as the lens on a parallel plane. Distance between lens and the top of grass canopy was maintained at 0.4 m for capturing the best representation of drought progression. All scans were performed using spectronon pro (Resonon Inc., Bozeman, MT, USA) software connected to the camera using a USB cable. Before performing a scan, the lens was appropriately focused, dark current noise was removed and the system was calibrated for reflectance measurement using a white tile provided by the manufacturer. To ensure distortion-free hyperspectral datacube with a unit-aspect-ratio image, benchtop system's swatch settings were adjusted using pixel aspect ratio calibration sheet also provided by the manufacturer. Once the system was ready, controlled- and stressed-pots were scanned individually every two days throughout the experiment. As the lens was focused centrally, obtained images were of the central grass area and were processed using spectronon pro data analysis software. The entire grass image was selected using a selection tool and the spectrum was generated. From each spectrum, vegetation indices were calculated either using built-in plugins or by manually creating algorithms. The list of vegetation indices calculated using image analysis is mentioned in Table 1.

    Table 1.  List of vegetation indices calculated using hyperspectral and multispectral image analysis for drought stress monitoring in Kentucky bluegrass. Name and number in subscript following the letter R in each formula represent the reflectance at individual light and particular wavelength.
    Vegetation indexIndex abbreviation and formula
    Hyperspectral analysisMultispectral analysis
    Structure Independent Pigment IndexSIPI = (R800 – R445) / (R800 + R680)SIPIm = (RNIR840 – RBlue444) / (RNIR840 + RRed668)
    Simple Ratio IndexSRI = R800 / R675SRIm = RNIR840 / RRed668
    Plant Senescence Reflectance IndexPSRI = (R680 –R500) / R750PSRIm = (RRed668 – RBlue475) / RRededge740
    Photochemical Reflectance IndexPRI = (R570 – R531) / (R570 + R531)PRI = (RGreen560 – RGreen531) / (RGreen560 + RGreen531)
    Normalized Difference Vegetation IndexNDVI = (R800 – R680) / (R800 + R680)NDVIm = (RNIR840 – RRed668) / (RNIR840 + RRed668)
    Normalized Difference Red EdgeNDRE = (R750 – R705) / (R750 + R705)NDREm = (RRededge717 – RRed668) / (RRededge717 + RRed668)
     | Show Table
    DownLoad: CSV
    Multispectral imaging of phenotypic responses to drought stress

    Micasense Rededge-MX dual camera system (AgEagle Sensor Systems Inc., Wichita, KS, USA) was used to collect multispectral images of controlled- and drought stressed-pots placed within a light box (1.2 m × 0.6 m × 0.6 m). The multispectral camera system had 1,280 × 960 resolution, 47.2° field of view and 5.4 mm focal length. The camera captured ten different spectral bands simultaneously on a command (Table 2). To allow the multispectral camera system, which was designed for aerial operation, to work in the light box settings, a downwelling light sensor (DLS) module provided by the manufacturer was installed to the camera system. Images were captured manually through WIFI connection from mobile devices or computer to the multispectral camera system. The sensor layout of the dual camera system, while causing negligible error in aerial condition, led to mismatching between spectral bands in a close distance, therefore, spectral bands needed to be overlapped during post-processing. The captured images of individual spectral bands were stored as separate .jpgf image files and then were used to calculate the relevant vegetation indices. Multispectral image analysis was executed using Python (Version 3.10) code by Rublee et al.[29]. Image analysis aligned ten spectral bands using Oriented FAST and Rotated BRIEF algorithm to achieve complete overlap between spectral band images. The reflectance correction panel provided by the manufacturer was used to account for the illumination condition in light box environment and the correction was reflected in pixel value adjustment for each band in python code; vegetation indices based on the aligned images were then calculated using the corresponding formula (Table 1). Images that included background noise were excluded from analysis.

    Table 2.  Spectral band details (center wavelength and band width) for Micasense Rededge-MX dual camera system.
    Band nameCentral wavelength (nm)Band width (nm)
    Blue44444428
    Blue47547532
    Green53153114
    Green56056027
    Red65065016
    Red66866814
    RE70570510
    RE71771712
    RE74074018
    NIR84084257
     | Show Table
    DownLoad: CSV
    Chlorophyll fluorescence imaging of physiological response to drought stress

    Chlorophyll fluorescence images were taken using a pulse amplitude modulated fluorescence imaging system (FC 800-O/1010, Photon System Instruments, Drasov, Czech Republic). A high-speed charge-coupled device (CCD) camera was mounted on a robotic arm placed in the middle of LED light panels. The camera had 720 × 560 pixels spatial resolution, 50 frames per second frame rate and 12-bit depth. Four different LED light panels each of 20 cm × 20 cm size were equipped with 64 orange-red (617 nm) LEDs in three panels and 64 cool-white LEDs (6,500 k) in the rest of one panel. Before making measurements, plants were dark-adapted for 25 min in a dark room to open all PSII reaction centers. The distance between camera and the top of the grass canopy was maintained at 0.3 m while taking images to ensure optimum quality. Images were acquired following the Kautsky effect measured in a pulse amplitude modulated mode[30,31]. Briefly, dark-adapted plants were first exposed to non-actinic measuring light for 5 s to measure minimum fluorescence at the dark-adapted state (Fo). Plants were immediately exposed to 800 ms saturation pulse of 3,350 µmol·m−2·s−1 to measure maximum fluorescence after dark adaptation (Fm). They were kept under dark relaxation for 17 s and then exposed to actinic light 750 µmol·m−2·s−1 for 70 s. Plants were exposed to a series of saturating pulses at 8 s, 18 s, 28 s, 48 s and 68 s during their exposure to actinic light conditions and maximum fluorescence at different light levels and steady state were measured. They were kept under dark relaxation again for 100 s and irradiated with saturating pulses at 28 s, 58 s and 88 s during dark relaxation for measuring maximum fluorescence during the relaxation. Selected durations for each light and dark relaxation state were preset in default quenching-act2 protocol of the fluorescence imaging system. Fluorescence at different light levels and steady states were used to calculate several fluorescence parameters (Table 3).

    Table 3.  Chlorophyll fluorescence parameters calculated from pulse amplitude modulated fluorescence imaging system.
    Chlorophyll fluorescence parameterFormula
    Maximum photochemical efficiency of PSII (Fv / Fm)(Fm-Fo) / Fm
    Photochemical efficiency of open PSII centers
    (F'v / F'm)    		(F'm – F'o) / F'm
    Actual photochemical quantum yield of PSII centers Y(PSII)(F'm – Fs) / F'm
    Photochemical quenching coefficient (Puddle model; qP)(F'm – Fs) / (F'm – F'o)
    Photochemical quenching coefficient (Lake model; qL)qP × F'o / Fs
    Non-photochemical quenching coefficient (qN)(Fm-F'm) / Fm
    Non-photochemical quenching (NPQ)(Fm-F'm) / F'm
    Chlorophyll fluorescence decrease ratio (Rfd)(Fm-Fs) / Fs
     | Show Table
    DownLoad: CSV

    Statistical analysis

    The two-way repeated measure analysis of variance was performed to determine treatment effects and t-test was performed to compare control and drought stress treatments at a given day of measurement. Correlation analysis using all individual observations (five replications for each control and drought stress treatments) was performed to determine the relationship among all measured traits, vegetation indices and fluorescence parameters. Partial least square regression (PLSR) models were developed in SAS JMP (version 13.2; SAS Institute, Cary, NC, USA) for comparing hyperspectral, multispectral and chlorophyll fluorescence imaging in their overall associations with physiological assessments of drought stress. Vegetation indices and fluorescence parameters from individual imaging technologies were predictor variables, and turfgrass quality and leaf relative water content were response variables. A leave one out cross validation approach was used to develop the best performing partial least square model for each imaging technology. A model was first established with all predictor variables and the variable with the lowest importance was removed from the dataset and the model was rebuilt with the remaining variables. The rebuilt model was re-validated using leave one out cross validation and assessed checking root mean PRESS and percent variation explained for cumulative Y values. From each loop of operation, one variable was removed, and a new model was developed. The whole process ended when the last variable was removed and thus no more models could be developed. Finally, a series of models was obtained, and they were compared to identify a model with the highest accuracy for individual imaging technologies. The best performing model from each imaging technology was used to estimate turfgrass quality and leaf relative water content.

    Results

    Changes in soil water content, leaf relative water content, and visual turf quality during drought stress

    The initial soil water content prior to drought stress was maintained at the field capacity of 29% and remained at this level in the well-watered control treatment during the entire experimental period (20 d) (Fig. 1a). SWC in the drought treatment significantly decreased to below the well-watered treatment, beginning at 4 d, and declined to 5.8% by 20 d.

    Figure 1.  Drought stress affected turf quality, leaf relative water content and soil volumetric water content during 20 d of stress period in Kentucky bluegrass. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint

    Leaf RWC was ≥ 93% in all plants prior to drought stress and declined to a significantly lower level than that of the control plants, beginning at 10 d of treatment when SWC declined to 16% (Fig. 1b). TQ began to decrease to a significantly lower level than the that of the well-watered plants at 10 d of drought stress at RWC of 87% and SWC of 16%, and further declined to the minimally acceptable level of 6.0 at 16 d of drought stress when RWC decreased to 66% and SWC dropped to 8% during drought stress (Fig. 1c).

    Hyperspectral and multispectral imaging of phenotypic responses to drought stress

    Most hyperspectral imaging indices, including SIPI (Fig. 2a), SRI (Fig. 2b), PRI (Fig. 2d), NDVI (Fig. 2e) and NDRE (Fig. 2f) exhibited a declining trend during 20-d drought stress while PSRI (Fig. 2C) showed increases during drought stress. The index value of drought-stressed plants became significantly lower than that of the well-watered plants, beginning at 14 d for SIPI and SRI, 16 d for PRI and PSRI, and 18 d for NDVI and NDRE. The multispectral SIPIm and SRIm did not differ between drought-stressed plants from the control plants until 18 d of treatment (Fig. 3a, b) while NDVIm, NDREm , PRIm , and PSRIm values were significantly lower than those of well-watered control plants at 16 d of drought stress (Fig. 3c−f).

    Figure 2.  Vegetation indices generated by hyperspectral sensing and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint
    Figure 3.  Vegetation indices generated by multispectral image analysis and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought tress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
    DownLoad: Full-Size Img PowerPoint

    Chlorophyll fluorescence imaging of photosynthetic responses to drought stress

    Chlorophyll fluorescence indices detected drought damages in leaf photosynthesis systems, as shown by declines in different indices during drought stress (Fig. 4). Drought-stressed plants exhibited significant lower chlorophyll fluorescence levels than that of the well-watered plants, beginning at 12 d for NPQ (Fig. 4a), 16 d for Fv/Fm (Fig. 4b), and 18 d for F'V/F'm (Fig. 4c), Y(PSII) (Fig. 4d), qP (Fig. 4e), and qL (Fig. 4f). Separation between drought-stressed and well-watered plants were also evident in index- or parameter- generated images (Fig. 5).

    Figure 4.  Chlorophyll fluorescence parameters measured by pulse amplitude modulated fluorescence imaging system and detection of drought by these parameters in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points. NPQ, Non-photochemical quenching; Fv /Fm, Maximum photochemical efficiency of PSII; F'v/F'm, Photochemical efficiency of open PSII centers; Y(PSII), Actual photochemical quantum yield of PSII centers; qP, Photochemical quenching coefficient (Puddle model); qL, Photochemical quenching coefficient (Lake model); qN, Non-photochemical quenching coefficient; Rfd, Chlorophyll fluorescence decrease ratio.
    DownLoad: Full-Size Img PowerPoint
    Figure 5.  Maps generated by the three most drought sensitive indices and parameters [hyperspectral structure independent pigment index (SIPI), multispectral normalized difference vegetation index (NDVIm) and chlorophyll fluorescence NPQ]. These maps clearly separated control and drought stress after 18 d of treatment when majorities of indices and parameters detected drought stress.
    DownLoad: Full-Size Img PowerPoint

    Correlation among individual imaging indices associated with phenotypic and photosynthetic responses to drought stress

    Leaf RWC and TQ had significant correlation with most of indices and parameters calculated using three different imaging sensors (hyperspectral, multispectral and chlorophyll fluorescence) (Table 4). Among the indices, RWC had the strongest correlations with chlorophyll fluorescence NPQ (r = 0.88) and qL (r = 0.89), hyperspectral PRI (r = 0.94), and multispectral PSRIm (−0.92). TQ was most correlated to chlorophyll fluorescence NPQ (r = 0.89), hyperspectral PSRI (r = −0.90), and multispectral PSRIm (r = −0.85).

    Table 4.  Correlations among several physiological traits, vegetation indices and chlorophyll fluorescence parameters.
    RWCTQFV/FmF'v/F'mY(PSII)NPQqNqPqLRfdSIPISRIPSRIPRINDVINDREWBISIPImPSRImPRImNDVImNDREm
    RWC1.00
    TQ0.95*1.00
    FV/Fm0.87*0.85*1.00
    F'v/F'm0.81*0.77*0.95*1.00
    Y(PSII)0.85*0.74*0.80*0.74*1.00
    NPQ0.88*0.89*0.95*0.84*0.75*1.00
    qN0.84*0.83*0.96*0.84*0.77*0.96*1.00
    qP0.82*0.70*0.73*0.66*0.99*0.69*0.72*1.00
    qL0.89*0.81*0.90*0.86*0.97*0.83*0.86*0.95*1.00
    Rfd0.84*0.82*0.89*0.83*0.77*0.92*0.86*0.72*0.83*1.00
    SIPI0.84*0.71*0.63*0.58*0.51*0.57*0.69*0.48*0.60*0.46*1.00
    SRI0.57*0.62*0.44*0.45*0.330.41*0.45*0.300.400.330.83*1.00
    PSRI−0.83*−0.90*−0.90*−0.86*−0.76*−0.83*−0.87*−0.71*−0.86*−0.76*−0.75*−0.57*1.00
    PRI0.94*0.82*0.80*0.76*0.71*0.79*0.71*0.66*0.77*0.78*0.260.17−0.78*1.00
    NDVI0.53*0.65*0.41*0.43*0.41*0.42*0.400.380.43*0.42*0.50*0.42*−0.54*0.311.00
    NDRE0.64*0.73*0.64*0.63*0.45*0.54*0.64*0.400.56*0.44*0.92*0.85*−0.75*0.330.50*1.00
    SIPIm0.52*0.50*0.56*0.58*0.47*0.52*0.49*0.43*0.52*0.51*0.330.28−0.58*0.61*0.270.39−0.281.00
    PSRIm−0.92*−0.85*−0.85*−0.85*−0.83*−0.80*−0.77*−0.79*−0.88*−0.77*−0.40−0.230.77*−0.82*−0.41−0.400.32−0.52*1.00
    PRIm0.20−0.030.06−0.010.280.140.110.310.200.180.050.100.01−0.040.000.090.060.09−0.041.00
    NDVIm0.75*0.74*0.77*0.78*0.67*0.72*0.68*0.62*0.73*0.70*0.43*0.33−0.76*0.81*0.370.47*−0.350.93*−0.76*−0.051.00
    NDREm0.90*0.89*0.89*0.89*0.81*0.83*0.81*0.76*0.88*0.81*0.52*0.41*−0.87*0.87*0.45*0.53*−0.320.62*−0.87*−0.040.85*1.00
    Details for individual abbreviations of vegetation indices and fluorescence parameters used in this table were previously mentioned in Tables 1 & 3. Some other abbreviations are: RWC, leaf relative water content; and TQ, turfgrass quality. Values followed by * indicate significant correlation at p ≤ 0.05. Correlation analysis was performed using all individual data points (five replications for each control and drought stress treatments).
     | Show Table
    DownLoad: CSV

    Correlations among different vegetation indices and parameters were also significant in many cases. Hyperspectral indices such as PSRI and PRI were significantly correlated with all multispectral indices except PRIm. Multispectral NDVIm and NDREm were significantly correlated with all hyperspectral indices. When hyperspectral and multispectral indices were correlated with chlorophyll fluorescence parameters, majorities of these indices significantly associated with fluorescence parameters with exceptions of multispectral PRIm which had weak and positive (r ranges 0.06 to 0.31) associations with fluorescence parameters.

    Prediction of turf quality and leaf relative water content using integrated indices from hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence

    Partial least square regression models were developed by integrating all indices from individual imaging technologies which identified the most reliable imaging systems to detect and monitor plant responses to drought stress. The PLSR model developed using hyperspectral imaging indices had improved predictability (root mean PRESS ≤ 0.38 and percent variation explained ≥ 87) compared to such models developed using other imaging systems and associated indices (Table 5). Comparing multispectral imaging with chlorophyll fluorescence imaging, multispectral imaging had slightly better predictability [root mean PRESS = 0.40 (RWC) and 0.44 (TQ) and percent variation explained = 86 (RWC) and 83 (TQ)] considering similar number of predictor variables used for estimating TQ and RWC in all imaging systems.

    Table 5.  Summary of partial least square model showing predictability of individual models using specific numbers of predictor variables (identified by leave one out cross validation) generated by different sensing technologies. Details of individual abbreviations are mentioned in previous tables. Partial least square was performed using all individual data points (five replications for each control and drought stress treatments).
    Sensing technology used for predictionPredicted
    variable    		No. of predictors usedPredictor variablesRoot mean
    PRESS    		Percent variation explained
    for cumulative Y    		Cumulative Q2
    HyperspectralTQ4PRI, PSRI, NDRE, SIPI0.36870.99
    RWC4PRI, PSRI, NDRE, SIPI0.38890.99
    MultispectralTQ3PSRIm, NDVIm, NDREm0.44850.97
    RWC3PSRIm, NDVIm, NDREm0.40860.97
    Chlorophyll fluorescenceTQ4Fv/Fm, NPQ, qN, qL0.46830.95
    RWC3Fv/Fm, NPQ, qL0.59840.93
     | Show Table
    DownLoad: CSV

    The integrated indices from each of the three imaging systems were highly correlated to TQ, with R2 of 0.90, 0.85, and 0.83 for hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence, respectively (Fig. 6). For RWC, the correlation R2 was 0.88, 0.84, and 0.80, respectively with hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence. The hyperspectral imaging was better be able to predict TQ and RWC compared to other imaging systems (Fig. 6).

    Figure 6.  Comparison of predicted turfgrass quality (TQ) and leaf relative water content (RWC) versus their measured values using partial least square regression model. Turfgrass quality and relative water contents were predicted using various indices generated by hyperspectral, multispectral and chlorophyll fluorescence sensing technologies. The dashed line represents the I:I line. Regression analysis was performed using all individual data points (five replications for each control and drought stress treatments).
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Leaf RWC and TQ are the two most widely used parameters or traits to evaluate turfgrass responses to drought stress[28,32,33]. In this study, RWC detected water deficit in leaves at 10 d of drought stress when SWC declined to 16% and TQ declined to below the minimal acceptable level of 6.0 at 16 d of drought stress when RWC decreased to 66% and SWC dropped to 8% during drought stress. These results suggested that RWC was a sensitive trait to detect water stress in plants, which is in agreement with previous research[34,35]. However, leaf RWC is a destructive measurement and TQ is a subjective estimate. Nondestructive and quantitative detection of stress symptoms in plants through assessing changes in phenotypic and physiological responses of plants to drought stress is critical for developing water-saving irrigation programs and breeding selection traits to increase water use efficiency and improve plant tolerance to drought stress. In this study, some of the phenotypic traits assessed by hyperspectral and multispectral imaging analysis and photosynthetic parameters measured by chlorophyll fluorescence were highly correlated to leaf RWC or visual TQ, as discussed in detail below, which could be used as non-destructive indicators or predictors for the level of drought stress in Kentucky bluegrass and other cool-season turfgrass species.

    The strong correlation of integrated indices from each of the three imaging systems with TQ (R2 of 0.90, 0.85, and 0.83, respectively) and RWC (R2 of 0.88, 0.84, and 0.80, respectively) for hyperspectral imaging, multispectral imaging, and chlorophyll fluorescence suggested that all three non-destructive imaging systems could be used as a non-destructive technique to detect and monitor water stress in Kentucky bluegrass. However, the hyperspectral imaging indices had higher predictability to RWC and visual TQ compared to the indices from multispectral imaging and chlorophyll fluorescence based on the PLSR models. Hyperspectral imaging used in this study captured images in 447 different spectral bands and gathered much more details about individual components of entire vegetation as each component has its own spectral signature. Multispectral imaging captures images with ten spectral bands and chlorophyll fluorescence imaging used only emitted red and far-red lights to snap images. Nevertheless, our results suggested that the PLSR models by integrating all indices from each individual imaging technologies identified the most reliable imaging systems to detect and monitor plant responses to drought stress in this study.

    The indices derived from the three imaging systems varied in their correlation to RWC or TQ in Kentucky bluegrass in this study. Among the indices, RWC had the strongest correlations with chlorophyll fluorescence NPQ (r = 0.88) and qL (r = 0.89), hyperspectral PRI (r = 0.94), and multispectral PSRIm (r = −0.92). TQ was most correlated to chlorophyll fluorescence NPQ (r = 0.89), hyperspectral PSRI (r = −0.90), and multispectral PSRIm (r = −0.85). The indices also varied in their sensitivity to drought stress for Kentucky bluegrass, and therefore they detected drought stress in plants at different times of treatment. The hyperspectral SIPI and SRI were the most responsive to drought stress with significant decline at 14 d followed by PRI and PSRI at 16 d while NDVI and NDRE were slowest showing decline (18 d) in response to drought. Multispectral indices exhibited decline later during drought at 16 d of drought stress for NDVIm, NDREm , PRIm , and PSRIm and 18 d for SIPIm and SRIm. Indices SIPI and SRI are related to leaf carotenoid composition and vegetation density and high spectral resolution of hyperspectral system was able to capture subtle changes in pigment concentration and canopy (slight leaf shrinking and rolling) at early phase of drought progression[36,37]. Index PSRI is indicative of the ratio of bulk carotenoids including α- and β-carotenes to chlorophylls and PRI is sensitive to xanthophyll cycle particularly de-epoxidation of zeaxanthin that releases excess energy as heat in order to photoprotection[38−40]. Activation of photoprotective mechanisms including xanthophyl cycle require a certain level of stress severity depending on type of abiotic stress and plant species[41]. The PSRI calculated using both hyperspectral and multispectral imaging systems exhibited similar trends, and PSRI and PRI from either imaging system detected drought stress after 16 days of treatment applications. In agreement with our results, Das & Seshasai[42] found that PSRI showed similar trends when its value > −0.2 regardless of whether measured using hyperspectral or multispectral imaging. Both PSRI and PRI were also highly correlated to leaf RWC or TQ in Kentucky bluegrass exposed to drought stress in this study, suggesting that these two indices could be useful parameters to detect and monitor plant responses to drought stress.

    Vegetation index of NDVI has been the most widely used vegetation index in several crops such as wheat (Triticum aestivum L.)[43], cool- and warm-season turfgrass species including perennial ryegrass (Lolium perenne L.), tall fescue (Festuca arundinacea Schreb.), seashore paspalum (Paspalum vaginatum Sw.) and hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt-Davy][2, 44, 45]. For example, Bhandari et al.[43] and Badzmierowski et al.[14] found NDVI was correlated to overall turfgrass quality and chlorophyll content under nitrogen and drought stresses in tall fescue and citrus (Citrus spp.) plants. In this study, NDVI and NDRE were also correlated to leaf RWC and TQ, both NDVI and NDRE calculated from hyperspectral or multispectral imaging were least responsive to drought stress or detected drought stress later than other indices. Hong et al.[46] reported that NDVI being a better indicator than NDRE for early drought stress detection in turfgrasses when these indices were measured by handheld multispectral sensor. Eitel et al.[47] utilized broadband satellite images to estimate NDVI and NDRE and identified NDRE being a better option for early detection of stress condition in woodland area. Either NDVI or NDRE could be used as indices for vegetation density, but not sensitive indicators for plant responses to drought stress or for detecting drought damages in plants.

    Chlorophyll fluorescence reflects the integrity and functionality of photosystems in the light reactions of photosynthesis and serves as a good indicator for photochemical activity and efficiency[48]. The Y(PSII) is an effective quantum yield of photochemical energy conversion and estimates the actual proportion of absorbed light that is used for electron transport[49]. The ratio of F'v/F'm is maximum proportion of absorbed light that can be used for electron transport when all possible PSII reaction centers are open under light adapted state. Parameters qP and qL estimate the fraction of open PSII centers based on 'puddle' and 'lake or connected unit' models of photosynthetic antenna complex, respectively[50]. Rfd is an indicator for photosynthetic quantum conversion associated with functionality of the photosynthetic core unit. Overall, these parameters revolve around the operation status and functioning of PSII reaction centers or the core unit that possesses chlorophyll a-P680 in a matrix of proteins[51]. Parameter NPQ indicates non-photochemical quenching of fluorescence via heat dissipation involving xanthophyll cycle and state transition of photosystems[52]. This parameter is mostly associated with xanthophylls and other pigments in light harvesting antenna complex of photosystems but not with the PSII core unit[53]. Li et al.[9] reported that chlorophyll fluorescence imaging parameters including F'V/F'm have a limitation of late drought detection in plants. Shin et al.[54] reported F'V/F'm, Y(PSII), qP, and qL detected stress effects under severe drought when leaves were completely wilted and fresh weights declined in lettuce (Lactuca sativa L.) seedings. In this study, NPQ and Fv/Fm exhibited significant decline earlier (12−16 d of stress treatment) when drought was in mild to moderate level (> 60% leaf water content) compared to other chlorophyll fluorescence indices. The NPQ was strongly correlated to leaf RWC (r = 0.88) and TQ (r = 0.89) for Kentucky bluegrass exposed to drought stress. These results suggested that NPQ is a sensitive indicator of photosynthetic responses to drought stress and could be a useful parameter for evaluating plant tolerance to drought stress and monitoring drought responses.

    Conclusions

    The comparative analysis of phenotypic and photosynthetic responses to drought stress using three imaging technologies (hyperspectral, multispectral and chlorophyll fluorescence) using the partial least square modeling demonstrated that the integrated vegetation indices from hyperspectral imaging had higher predictability for detecting turfgrass responses to drought stress relative to those from multispectral imaging and chlorophyll fluorescence. Among individual indices, SIPI and SRI from hyperspectral imaging were able to detect drought stress sooner than others while PSRI and PRI from both hyperspectral and multispectral imaging were also highly correlated to leaf RWC or TQ responses to drought stress, suggesting these indices could be useful parameters to detect and monitor drought stress in cool-season turfgrass. While NDVI or NDRE from both hyperspectral and multispectral imaging could be used as indices for vegetation density, but not sensitive indicators for plant responses to drought stress. Among chlorophyll fluorescence indices, NPQ and Fv/Fm were more closely correlated to RWC or TQ while NPQ was most responsive to drought stress, and therefore NPQ could be a useful indicator for detecting and monitoring cool-season turfgrass response to drought stress. The sensitivity and effectiveness of these indices associated with drought responses in this study could be further testified in other cool-season and warm-season turfgrass species under field conditions. As each imaging technology used in this experiment comes with bulky accessories such as LED panels, mounting tower and support system, capturing images within limited space of controlled environmental chambers are challenging. Future research should be in developing multimodal imaging integrating major features of all three technologies and reducing size and space requirement that would deliver improved decision support for drought monitoring and irrigation management in turfgrasses. Development of advanced algorithms that could incorporate broader spectral details or band reflectance for calculating novel vegetation indices are warranted.

    Acknowledgments

    The research presented in this paper was funded by the United State Department of Agriculture - National Institute of Food and Agriculture (2021-51181-35855).

  • The authors declare that they have no conflict of interest. Bingru Huang is the Editorial Board member of Journal Grass Research who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and her research groups.

  • [1]

    Hu Y, Xiang W, Karina VR, Lei P, Deng X, et al. 2022. Photosynthetic and hydraulic traits influence forest resistance and resilience to drought stress across different biomes. Science of The Total Environment 828:154517

    doi: 10.1016/j.scitotenv.2022.154517

    CrossRef   Google Scholar

    [2]

    Kinoshita T, Kume A, Hanba YT. 2021. Seasonal variations in photosynthetic functions of the urban landscape tree species Gingko biloba: photoperiod is a key trait. Trees 35:273−85

    doi: 10.1007/s00468-020-02033-3

    CrossRef   Google Scholar

    [3]

    Fu YH, Li X, Chen S, Wu Z, Su J, et al. 2022. Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests. Global Change Biology 28:4935−46

    doi: 10.1111/gcb.16227

    CrossRef   Google Scholar

    [4]

    Zhang B, Zhou L, Zhou X, Bai Y, Zhan M, et al. 2022. Differential responses of leaf photosynthesis to insect and pathogen outbreaks: a global synthesis. Science of The Total Environment 832:155052

    doi: 10.1016/j.scitotenv.2022.155052

    CrossRef   Google Scholar

    [5]

    Kännaste A, Jürisoo L, Runno-Paurson E, Kask K, Talts E, et al. 2023. Impacts of Dutch elm disease-causing fungi on foliage photosynthetic characteristics and volatiles in Ulmus species with different pathogen resistance. Tree Physiology 43:57−74

    doi: 10.1093/treephys/tpac108

    CrossRef   Google Scholar

    [6]

    Bryant KN, Stenzel J, Mathias J, Kwon H, Kolden CA, et al. 2022. Boosts in leaf-level photosynthetic capacity aid Pinus ponderosa recovery from wildfire. Environmental Research Letters 17:114034

    doi: 10.1088/1748-9326/ac9cf2

    CrossRef   Google Scholar

    [7]

    Melki F, Talbi ZO, Jeder S, Louati F, Nouairi I, et al. 2022. Cadmium and lead excess differently affect growth, photosynthetic activity and nutritional status of Trigonella foenum-graecum L. Crop & Pasture Science 73:969−80

    doi: 10.1071/CP21583

    CrossRef   Google Scholar

    [8]

    Liu Z, Tian L, Chen M, Zhang L, Lu Q, et al. 2023. Hormesis responses of growth and photosynthetic characteristics in Lonicera japonica Thunb. to cadmium stress: whether electric field can improve or not? Plants 12:933

    doi: 10.3390/plants12040933

    CrossRef   Google Scholar

    [9]

    Zhou J, Song F, He Y, Zhang W, Liang X, et al. 2023. LncRNA evolution and DNA methylation variation participate in photosynthesis pathways of distinct lineages of Populus. Forestry Research 3:3

    doi: 10.48130/FR-2023-0003

    CrossRef   Google Scholar

    [10]

    Song Y and Jin G. 2023. Do tree size and tree shade tolerance affect the photosynthetic capacity of broad-leaved tree species? Plants 12:523

    doi: 10.3390/plants12030523

    CrossRef   Google Scholar

    [11]

    Schmiege SC, Griffin KL, Boelman NT, Vierling LA, Bruner SG, et al. 2023. Vertical gradients in photosynthetic physiology diverge at the latitudinal range extremes of white spruce. Plant, Cell & Environment 46:45−63

    doi: 10.1111/pce.14448

    CrossRef   Google Scholar

    [12]

    Kosugi Y, Takanashi S, Yokoyama N, Kamakura M. 2012. Vertical variation in leaf gas exchange parameters for a Southeast Asian tropical rainforest in Peninsular Malaysia. Journal of Plant Research 125:735−48

    doi: 10.1007/s10265-012-0495-5

    CrossRef   Google Scholar

    [13]

    Chen X, Sun J, Lyu M, Wang M, Hu D, et al. 2021. Prediction of photosynthetic light-response curves using traits of the leaf economics spectrum for 75 woody species: effects of leaf habit and sun–shade dichotomy. American Journal of Botany 108:423−31

    doi: 10.1002/ajb2.1629

    CrossRef   Google Scholar

    [14]

    Wyka TP, Oleksyn J, Żytkowiak R, Karolewski P, Jagodziński AM, et al. 2012. Responses of leaf structure and photosynthetic properties to intra-canopy light gradients: a common garden test with four broadleaf deciduous angiosperm and seven evergreen conifer tree species. Oecologia 170:11−24

    doi: 10.1007/s00442-012-2279-y

    CrossRef   Google Scholar

    [15]

    Coble AP, Cavaleri MA. 2014. Light drives vertical gradients of leaf morphology in a sugar maple (Acer saccharum) forest. Tree Physiology 34:146−58

    doi: 10.1093/treephys/tpt126

    CrossRef   Google Scholar

    [16]

    Coble AP, Vanderwall B, Mau A, Cavaleri MA. 2016. How vertical patterns in leaf traits shift seasonally and the implications for modeling canopy photosynthesis in a temperate deciduous forest. Tree Physiology 36:1077−91

    doi: 10.1093/treephys/tpw043

    CrossRef   Google Scholar

    [17]

    Liu Q, Li F. 2018. Spatial and seasonal variations of standardized photosynthetic parameters under different environmental conditions for young planted Larix olgensis Henry trees. Forests 9:522

    doi: 10.3390/f9090522

    CrossRef   Google Scholar

    [18]

    Martin RE, Asner GP, Bentley LP, Shenkin A, Salinas N, et al. 2019. Covariance of sun and shade leaf traits along a tropical forest elevation gradient. Frontiers in Plant Science 10:1810

    doi: 10.3389/fpls.2019.01810

    CrossRef   Google Scholar

    [19]

    Koch GW, Sillett SC, Jennings GM, Davis SD. 2004. The limits to tree height. Nature 428:851−54

    doi: 10.1038/nature02417

    CrossRef   Google Scholar

    [20]

    Hata Y, Kumagai T, Shimizu T, Miyazawa Y. 2023. Implications of seasonal changes in photosynthetic traits and leaf area index for canopy CO2 and H2O fluxes in a Japanese cedar (Cryptomeria japonica D. Don) plantation. Ecological Modelling 477:110271

    doi: 10.1016/j.ecolmodel.2022.110271

    CrossRef   Google Scholar

    [21]

    Collier DE, Thibodeau BA. 1995. Changes in respiration and chemical content during autumnal senescence of Populus tremuloides and Quercus rubra leaves. Tree Physiology 15:759−64

    doi: 10.1093/treephys/15.11.759

    CrossRef   Google Scholar

    [22]

    Wyka TP, Żytkowiak R, 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 135:483−93

    doi: 10.1007/s10342-016-0947-x

    CrossRef   Google Scholar

    [23]

    Yin H, Yang M, Li P, Yu X, 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 13:2067

    doi: 10.3390/f13122067

    CrossRef   Google Scholar

    [24]

    Shimada R, Takahashi K. 2022. Diurnal and seasonal variations in photosynthetic rates of dwarf pine Pinus pumila at the treeline in central Japan. Arctic, Antarctic, and Alpine Research 54:1−12

    doi: 10.1080/15230430.2021.2022995

    CrossRef   Google Scholar

    [25]

    Atkin OK, Bloomfield KJ, Reich PB, Tjoelker MG, Asner GP, et al. 2015. Global variability in leaf respiration in relation to climate and leaf traits. New Phytologist 206:614−36

    doi: 10.1111/nph.13253

    CrossRef   Google Scholar

    [26]

    Coble AP, Cavaleri MA. 2015. Light acclimation optimizes leaf functional traits despite height-related constraints in a canopy shading experiment. Oecologia 177:1131−43

    doi: 10.1007/s00442-015-3219-4

    CrossRef   Google Scholar

    [27]

    Xu M, Wang Q, Yang F, Zhang T, Zhu X, 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

    doi: 10.1016/j.ecolind.2022.109595

    CrossRef   Google Scholar

    [28]

    Zlobin IE, Kartashov AV, Pashkovskiy PP, Ivanov YV, Kreslavski VD, et al. 2019. Comparative photosynthetic responses of Norway spruce and Scots pine seedlings to prolonged water deficiency. Journal of Photochemistry and Photobiology B: Biology 201:111659

    doi: 10.1016/j.jphotobiol.2019.111659

    CrossRef   Google Scholar

    [29]

    Coble AP, 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 37:1337−51

    doi: 10.1093/treephys/tpx016

    CrossRef   Google Scholar

    [30]

    Xiong D, 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 72:5709−20

    doi: 10.1093/jxb/erab230

    CrossRef   Google Scholar

    [31]

    Li W, Li J, Wei J, Niu C, Yang D, et al. 2023. Response of photosynthesis, the xanthophyll cycle, and wax in Japanese yew (Taxus cuspidata L.) seedlings and saplings under high light conditions. PeerJ 11:e14757

    doi: 10.7717/peerj.14757

    CrossRef   Google Scholar

    [32]

    Mayoral C, Calama R, Sánchez-González M, 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

    doi: 10.1007/s11056-015-9471-y

    CrossRef   Google Scholar

    [33]

    Kitao M, Yazaki K, Tobita H, Agathokleous E, Kishimoto J, et al. 2022. Exposure to strong irradiance exacerbates photoinhibition and suppresses N resorption during leaf senescence in shade-grown seedlings of fullmoon maple (Acer japonicum). Frontiers in Plant Science 13:6413

    doi: 10.3389/fpls.2022.1006413

    CrossRef   Google Scholar

    [34]

    Hazrati S, Tahmasebi-Sarvestani Z, Modarres-Sanavy SAM, Mokhtassi-Bidgoli A, Nicola S. 2016. Effects of water stress and light intensity on chlorophyll fluorescence parameters and pigments of Aloe vera L. Plant Physiology and Biochemistry 106:141−48

    doi: 10.1016/j.plaphy.2016.04.046

    CrossRef   Google Scholar

    [35]

    Xu H, Xiao J, Zhang Z, Ollinger SV, Hollinger DY, et al. 2020. Canopy photosynthetic capacity drives contrasting age dynamics of resource use efficiencies between mature temperate evergreen and deciduous forests. Global Change Biology 26:6156−67

    doi: 10.1111/gcb.15312

    CrossRef   Google Scholar

    [36]

    He X, Si J, Zhou D, Wang C, Zhao C, et al. 2022. Leaf chlorophyll parameters and photosynthetic characteristic variations with stand age in a typical desert species (Haloxylon ammodendron). Frontiers in Plant Science 13:967849

    doi: 10.3389/fpls.2022.967849

    CrossRef   Google Scholar

    [37]

    Letts MG, Phelan CA, Johnson DRE, Rood SB. 2008. Seasonal photosynthetic gas exchange and leaf reflectance characteristics of male and female cottonwoods in a riparian woodland. Tree Physiology 28:1037−48

    doi: 10.1093/treephys/28.7.1037

    CrossRef   Google Scholar

    [38]

    Liu Q, Xie L, Dong L, Li F. 2021. Dynamic simulation of the multilayer crown net photosynthetic rate and determination of the functional crown for larch (Larix olgensis) trees. New Forests 52:1011−35

    doi: 10.1007/s11056-021-09839-0

    CrossRef   Google Scholar

    [39]

    Bassman JH, Zwier JC. 1991. Gas exchange characteristics of Populus trichocarpa, Populus deltoides and Populus trichocarpa × P. deltoids clones. Tree Physiology 8:145−59

    doi: 10.1093/treephys/8.2.145

    CrossRef   Google Scholar

    [40]

    R Core Team 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at https://www.R-project.org/.
    [41]

    Woodruff DR. 2014. The impacts of water stress on phloem transport in Douglas-fir trees. Tree Physiology 34:5−14

    doi: 10.1093/treephys/tpt106

    CrossRef   Google Scholar

    [42]

    Zhang Y, Equiza MA, Zheng Q, Tyree MT. 2012. Factors controlling plasticity of leaf morphology in Robinia pseudoacacia L. II: the impact of water stress on leaf morphology of seedlings grown in a controlled environment chamber. Annals of Forest Science 69:39−47

    doi: 10.1007/s13595-011-0134-7

    CrossRef   Google Scholar

    [43]

    Coble AP, Autio A, Cavaleri MA, Binkley D, Ryan MG. 2014. Converging patterns of vertical variability in leaf morphology and nitrogen across seven Eucalyptus plantations in Brazil and Hawaii, USA. Trees-structure and Function 28:1−15

    doi: 10.1007/s00468-013-0925-6

    CrossRef   Google Scholar

    [44]

    Ambrose AR, Sillett SC, Dawson TE. 2009. Effects of tree height on branch hydraulics, leaf structure and gas exchange in California redwoods. Plant, Cell & Environment 32:743−57

    doi: 10.1111/j.1365-3040.2009.01950.x

    CrossRef   Google Scholar

    [45]

    Weerasinghe LK, Creek D, Crous KY, Xiang S, Liddell MJ, et al. 2014. Canopy position affects the relationships between leaf respiration and associated traits in a tropical rainforest in Far North Queensland. Tree Physiology 34:564−84

    doi: 10.1093/treephys/tpu016

    CrossRef   Google Scholar

    [46]

    Mullin LP, Sillett SC, Koch GW, Tu KP, Antoine ME. 2009. Physiological consequences of height-related morphological variation in Sequoia sempervirens foliage. Tree Physiology 29:999−1010

    doi: 10.1093/treephys/tpp037

    CrossRef   Google Scholar

    [47]

    Puglielli G, Varone L, Gratani L, Catoni R. 2017. Specific leaf area variations drive acclimation of Cistus salvifolius in different light environments. Photosynthetica 55:31−40

    doi: 10.1007/s11099-016-0235-5

    CrossRef   Google Scholar

    [48]

    Oguchi R, Hikosaka K, Hirose T. 2005. Leaf anatomy as a constraint for photosynthetic acclimation: differential responses in leaf anatomy to increasing growth irradiance among three deciduous trees. Plant, Cell & Environment 28:916−27

    doi: 10.1111/j.1365-3040.2005.01344.x

    CrossRef   Google Scholar

    [49]

    Broeckx LS, Fichot R, Verlinden MS, Ceulemans R. 2014. Seasonal variations in photosynthesis, intrinsic water-use efficiency and stable isotope composition of poplar leaves in a short-rotation plantation. Tree Physiology 34:701−715

    doi: 10.1093/treephys/tpu057

    CrossRef   Google Scholar

    [50]

    Riikonen J, Oksanen E, Peltonen P, Holopainen T, Vapaavuori E. 2003. Seasonal variation in physiological characteristics of two silver birch clones in the field. Canadian Journal of Forest Research 33:2164−76

    doi: 10.1139/x03-136

    CrossRef   Google Scholar

    [51]

    Pieruschka R, Albrecht H, Muller O, Berry JA, Klimov D, et al. 2014. Daily and seasonal dynamics of remotely sensed photosynthetic efficiency in tree canopies. Tree Physiology 34:674−85

    doi: 10.1093/treephys/tpu035

    CrossRef   Google Scholar

    [52]

    Misson L, Tu KP, Boniello RA, Goldstein AH. 2006. Seasonality of photosynthetic parameters in a multi-specific and vertically complex forest ecosystem in the Sierra Nevada of California. Tree Physiology 26:729−41

    doi: 10.1093/treephys/26.6.729

    CrossRef   Google Scholar

    [53]

    Grassi G, Vicinelli E, Ponti F, Cantoni L, Magnani F. 2005. Seasonal and interannual variability of photosynthetic capacity in relation to leaf nitrogen in a deciduous forest plantation in northern Italy. Tree Physiology 25:349−60

    doi: 10.1093/treephys/25.3.349

    CrossRef   Google Scholar

    [54]

    Sperlich D, Chang CT, Peñuelas J, Gracia C, Sabaté S. 2015. Seasonal variability of foliar photosynthetic and morphological traits and drought impacts in a Mediterranean mixed forest. Tree Physiology 35:501−20

    doi: 10.1093/treephys/tpv017

    CrossRef   Google Scholar

    [55]

    Pardos M, Puértolas J, Madrigal G, Garriga E, De Blas S, et al. 2010. Seasonal changes in the physiological activity of regeneration under a natural light gradient in a Pinus pinea regular stand. Forest Systems 19:367−80

    doi: 10.5424/fs/2010193-9102

    CrossRef   Google Scholar

    [56]

    Kunert N, Hajek P, Hietz P, Morris H, Rosner S, et al. 2022. Summer temperatures reach the thermal tolerance threshold of photosynthetic decline in temperate conifers. Plant Biology 24:1254−61

    doi: 10.1111/plb.13349

    CrossRef   Google Scholar

    [57]

    Yasumura Y, Hikosaka K, Hirose T. 2006. Seasonal changes in photosynthesis, nitrogen content and nitrogen partitioning in Lindera umbellata leaves grown in high or low irradiance. Tree Physiology 26:1315−23

    doi: 10.1093/treephys/26.10.1315

    CrossRef   Google Scholar

    [58]

    Rodríguez-calcerrada J, Atkin OK, Robson TM, Zaragoza-castells J, Gil L, et al. 2010. Thermal acclimation of leaf dark respiration of beech seedlings experiencing summer drought in high and low light environments. Tree Physiology 30:214−24

    doi: 10.1093/treephys/tpp104

    CrossRef   Google Scholar

    [59]

    Radoglou K, Teskey RO. 1997. Changes in rates of photosynthesis and respiration during needle development of loblolly pine. Tree Physiology 17:485−88

    doi: 10.1093/treephys/17.7.485

    CrossRef   Google Scholar

    [60]

    Vose JM, Ryan MG. 2002. Seasonal respiration of foliage, fine roots, and woody tissues in relation to growth, tissue N, and photosynthesis. Global Change Biology 8:182−93

    doi: 10.1046/j.1365-2486.2002.00464.x

    CrossRef   Google Scholar

    [61]

    Walters RG. 2005. Towards an understanding of photosynthetic acclimation. Journal of Experiment Botany 56:435−47

    doi: 10.1093/jxb/eri060

    CrossRef   Google Scholar

    [62]

    Way DA, Sage RF. 2008. Thermal acclimation of photosynthesis in black spruce [Picea mariana (Mill.) B.S.P.]. Plant, Cell & Environment 31:1250−62

    doi: 10.1111/j.1365-3040.2008.01842.x

    CrossRef   Google Scholar

    [63]

    Wieser G, Oberhuber W, Walder L, Spieler D, Gruber A. 2010. Photosynthetic temperature adaptation of Pinus cembra within the timberline ecotone of the Central Austrian Alps. Annals of Forest Science 67:201

    doi: 10.1051/forest/2009094

    CrossRef   Google Scholar

    [64]

    Han Q, Kawasaki T, Katahata S, Mukai Y, Chiba Y. 2003. Horizontal and vertical variations in photosynthetic capacity in a Pinus densiflora crown in relation to leaf nitrogen allocation and acclimation to irradiance. Tree Physiology 23:851−57

    doi: 10.1093/treephys/23.12.851

    CrossRef   Google Scholar

    [65]

    Fellner H, Dirnberger GF, Sterba H. 2016. Specific leaf area of European Larch (Larix decidua MILL.). Trees 30:1237−44

    doi: 10.1007/s00468-016-1361-1

    CrossRef   Google Scholar

    [66]

    Meinzer FC, Bond BJ, Karanian JA. 2008. Biophysical constraints on leaf expansion in a tall conifer. Tree Physiology 28:197−206

    doi: 10.1093/treephys/28.2.197

    CrossRef   Google Scholar

    [67]

    Han Q. 2011. Height-related decreases in mesophyll conductance, leaf photosynthesis and compensating adjustments associated with leaf nitrogen concentrations in Pinus densiflora. Tree Physiology 31:976−84

    doi: 10.1093/treephys/tpr016

    CrossRef   Google Scholar

    [68]

    Marshall JD, Monserud RA. 2003. Foliage height influences specific leaf area of three conifer species. Canadian Journal of Forest Research 33:164−70

    doi: 10.1139/x02-158

    CrossRef   Google Scholar

  • Cite this article

    Liu Q, Zhang Z, Wang D, Li F, Xie L. 2024. Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees. Forestry Research 4: e001 doi: 10.48130/fr-0023-0029
    Liu Q, Zhang Z, Wang D, Li F, Xie L. 2024. Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees. Forestry Research 4: e001 doi: 10.48130/fr-0023-0029
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Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees

  • 1.

    School of Forestry, Hebei Agricultural University, Baoding 071000, PR China

  • 2.

    School of Forestry, Northeast Forestry University, Harbin 150040, PR China

  • 3.

    School of Forestry, Beihua University, Jilin 132000, PR China

  • Received: 05 September 2023
  • Revised: 15 November 2023
  • Accepted: 22 November 2023
  • Published online: 09 January 2024
Forestry Research  4,  Article number: e001  (2024)  |  Cite this article

Abstract: 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 throughout the entire growing season. Vertical and seasonal patterns of leaf photosynthetic characteristics and their impact factors were analyzed. Results showed that maximum net CO2 assimilation (Amax), light saturated stomatal conductance (gs-sat), respiration rate (RD), needle mass per area (NMA), and ratio of needle length to needle width (rlw) 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. Amax and gs-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 trees.

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    Introduction

    • Photosynthetic characteristics are indicative of physiological parameters that correlate with forest primary production[1] and drive carbon uptake[2]. They directly participate in the 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], causing leaves to alter their structure and physiological function to adapt to the lighting environment. Likewise differences appear between shade and sun leaves within the crown[13]. Sun leaves have a high maximum net CO2 assimilation (Amax), respiration rate (RD), 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], as it represents the ability to transport the water from the root to the crown. Crown photosynthetic characteristics also show seasonal variation with individual development and environmental changes[20,21]. For example, Amax usually shows a parabolic seasonal variation pattern due to its positive correlation with the temperature (T), solar radiation, and soil moisture[22−24]. However, RD exhibits an opposite 'U'-shaped trend[18] due to the decline of cytochrome-mediated respiration[21] and temperature sensitivity[25].

      Studies have shown that spatial and seasonal variations in canopy photosynthetic characteristics are closely associated with the comprehensive impact of light[26], temperature[24,27], humidity[28], and seasonal patterns of leaf structural traits[29,30]. Generally, adequate light and suitable temperature and humidity can promote photosynthesis, by improving light energy utilization[31] and photosynthetic enzyme activity[32], however, some environmental conditions will inhibit photosynthesis[33]. For example, the common natural phenomena 'midday depressions' is a self protection mechanism by regulating blade osmotic pressure and maintaining mesophyll cell activity, in response to the stress of strong light, high temperature and low humidity[34]. Additionally, the age[22,35,36] and sex of dioecious tree species[37] 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 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 of China 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 in photosynthetic characteristics of different crown positions, growing periods and tree individuals? Secondly, what is the variation patterns of photosynthetic characteristics within the tree crown during the growing season? Finally, we provide a comprehensive assessment of the relationships between photosynthetic characteristics and crown structure, leaf morphology and environmental conditions.

    Materials and methods

      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 d of frost-free period, an average annual precipitation of 700 mm, 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 20 m × 30 m with same site quality in the young L. olgensis plantation are set up, and the trees with a diameter at breast height (DBH) larger than 5 cm in each plot were measured. The specific measurement factors include tree height (H), DBH, crown width (CW) and the relative coordinates (xi, yi) of each tree were investigated. Then the average DBH of five plots were calculated according to the per tree measurement data. A scaffold was built around the sample tree, ensuring that the sample tree was completely surrounded by 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 walkway. After each measurement, the upper tread was removed to avoid the influence on the measurement result caused by the blocking of light. The crown length of each sample tree was divided into several vertical sections based on the whorls from tree top to bottom and numbering began from V1st. Three healthy and fully expanded needles 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 Liu et al.[38]. 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 the month and mid-month) 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 was acclimated for 20 min at a CO2 concentration of 390 ppm with a CO2 mixer (6400-01, LI-COR, Inc., Logan, NE, USA) to maintain a stable CO2 supply. All sample cluster needles were acclimated under a PAR of 1,400 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 was measured at 10 PAR gradients: 2,000, 1,500, 1,200, 1,000, 500, 200, 150, 100, 50, 0 μmol·m−2·s−1. Sample cluster needles were allowed to equilibrate for a minimum of 2 min at each measurement before data was 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) were recorded. After the measurements, 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) was 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 needle mass per area (NMA, g·m−2). Each cluster sample was scanned immediately after collection and then surveyed with an image analysis software (Image-Pro Plus 6.0, Media Cybernetics, Inc., Bethesda, USA), resulting in the projected needle area (NA, m2), needle length (l) and needle width (w), and consequently obtained 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 CO2 assimilation (Amax, μmol·m−2·s−1) and dark respiration (RD, μmol·m−2·s−1) were estimated from the PLR curves using the modified Mitscherlich model[39]:

      An=Amax×(1−e(−α×PAR/Amax))−RD (1)

      where An is the net CO2 assimilation (μmol·m−2·s−1), Amax is the light-saturated net CO2 assimilation (μmol·m−2·s−1), α is the apparent quantum yield, PAR is the photosynthetically active radiation (μmol·m−2·s−1), and RD is the dark respiration rate (μmol·m−2·s−1).

      The light saturated stomatal conductance (gs-sat, mol·m−2·s−1) was determined as the corresponding gs value of Amax. Water-use efficiency (WUEsat, mmol CO2 mol H2O−1) was calculated as the ratio of Amax to gs-sat. As the environment conditions were not maintained under a certain value during the measurement of PLR curves except CO2 concentration (stabilized at 390 ppm).

      Statistical analysis

    • Table 1 shows the data summary. Statistical analyses were performed using R software 4.2.2[40]. A three-way repeated-measures 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 CO2 assimilation (Amax), dark respiration (RD), light-saturated stomatal conductance (gs-sat), and water-use efficiency (WUEsat). 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.

      Table 1.  Sample tree and data summary. Photosynthetic light response curves (752) were investigated, including 9303 instantaneous environmental and functional factors, from 36 pseudowhorls from five planted Larix olgensis trees.

      StatisticsNet photosynthetic rateLeaf traitEnvironmental conditionsSpatial position
      An (μmol·m−2·s−1)LMA (g·m−2)Tair (°C)VPD (kPa)PAR (μmol·m−2·s−1)RDINC
      No.9303752
      Mean5.2358.527.91.77570.52
      Std.4.4720.13.677230.26
      Max.27.49127.539.44.322000.99
      Min.−3.9014.315.50.500.08
      An, net CO2 assimilation; 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.
    Results

      Vertical profiles of photosynthetic and morphological parameters of needles

    • Photosynthetic parameters (Amax, RD, gs-sat and WUEsat) and morphological parameters (LMA and rlw) 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 WUEsat of the five sampled trees followed the opposite trend (Fig. 1f). Amax significantly decreased with RDINC (Fig. 1a), and the mean Amax 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 gs-sat, NMA, and RD (Table 2), their tendencies demonstrated a pronounced decrease with increasing RDIINC (Fig. 1b, c, & d). The mean values of RD, gs-sat, and NMA varied by 2.5-fold, 3.3-fold, and 2.3-fold, respectively, from the top to the bottom of the crown. Mean rlw exhibited a slight decrease with RDINC when RDINC was lower than 0.3 (upper crown) (Fig. 1e) but then sharply decreased when RDINC was greater than 0.4 (middle and lower crown). In contrast, mean WUEsat showed a weak upward trend with increasing RDINC (Fig. 1f), varying by only 0.015 mmol CO2 mol H2O−1 from top to bottom.

      Table 2.  Results of the three-way repeated measures ANOVA of photosynthetic and morphological parameters.

      EffectsdfAmaxgs-satRDWUEsatNMArlw
      T*******************
      P*********************
      L*********************
      T×P*******************
      T×L****************
      P×L*******************
      T×P×L***********
      P, measurement period; T, tree specific; L, crown location. The different parameters have been identified and described in the text. *, 0.01 < p ≤ 0.05; **, 0.001 < p ≤ 0.01; ***, p ≤ 0.001.

      Figure 1. 

      Vertical profiles of (a) light-saturated net photosynthetic rate (Amax); (b) light-saturated stomatal conductance (gs-sat); (c) needle mass per area (NMA); (d) dark respiration (RD); (e) ratio of length to width of needles (rlw) and (f) light-saturated water use efficiency (WUEsat). Data points represented seasonal mean values (solid bars represented stand error). Black solid line represented mean values of five sample trees (a)−(f).

      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. rlw was the only parameter that showed a significant vertical difference across the entire growth season (upper > middle > lower crown). Amax, gs-sat, and NMA demonstrated a similar vertical pattern in June, but the mean values of Amax and gs-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. RD showed no significant difference with respect to vertical location in the crown during the early period of needle expansion but showed significantly greater values in the upper crown than in the middle and lower crowns after June. WUEsat only showed a slight vertical difference at PVI, PVII, and PIV.

      Table 3.  Summary of physiological and morphological parameters in upper, middle, and lower crown, respectively during growing seasons.

      FactorsLocationPIPIIPIIIPIVPVPVIPVIIPVIII
      Amax
      (μmol·m−2·s−1)      		Upper7.07 ± 0.18a8.67 ± 0.51a9.77 ± 0.89a12.11 ± 1.48a12.16 ± 0.74a14.18 ± 1.33a13.74 ± 1.42a11.42 ± 0.85a
      Middle6.76 ± 0.23a7.19 ± 0.66b7.82 ± 0.75b9.19 ± 0.82b9.03 ± 0.54b9.03 ± 1.24b8.11 ± 0.77b7.88 ± 0.38b
      Lower5.80 ± 0.12b5.93 ± 1.1c6.57 ± 0.78c6.03 ± 0.97c6.48 ± 1.07c5.15 ± 1.19c4.3 ± 1.71c5.47 ± 0.86c
      RD
      (μmol·m−2·s−1)      		Upper1.51 ± 0.12a1.35 ± 0.23a1.25 ± 0.12a1.13 ± 0.28a1.35 ± 0.25a1.36 ± 0.19a1.30 ± 0.15a1.46 ± 0.10a
      Middle1.37 ± 0.11a1.01 ± 0.25b0.96 ± 0.17b0.80 ± 0.18b0.66 ± 0.11b0.99 ± 0.23b0.90 ± 0.14b1.11 ± 0.14b
      Lower1.46 ± 0.03a0.74 ± 0.27c0.83 ± 0.20b0.65 ± 0.13b0.51 ± 0.10b0.96 ± 0.44b0.71 ± 0.20c1.04 ± 0.18b
      gs-sat
      (mol·m−2·s−1)      		Upper0.090 ± 0.01a0.117 ± 0.03a0.118 ± 0.01a0.164 ± 0.02a0.168 ± 0.02a0.297 ± 0.06a0.241 ± 0.05a0.223 ± 0.02a
      Middle0.074 ± 0.01a0.082 ± 0.02b0.089 ± 0.01b0.109 ± 0.01b0.114 ± 0.01b0.174 ± 0.04b0.125 ± 0.02b0.132 ± 0.02b
      Lower0.047 ± 0.01b0.058 ± 0.02c0.075 ± 0.02c0.078 ± 0.01c0.083 ± 0.02c0.117 ± 0.03c0.063 ± 0.02c0.085 ± 0.03c
      WUEsat
      (mmol·mol−1)      		Upper92.1 ± 9.6a93.3 ± 22.1a102.1 ± 11.4a79.7 ± 8.0ab76.0 ± 9.5a58.9 ± 8.1b61.1 ± 6.6b62.5 ± 3.8a
      Middle92.0 ± 7.2a91.0 ± 22.5a94.0 ± 7.0a84.2 ± 6.0a88.0 ± 9.3a69.5 ± 8.4a71.7 ± 12.3a63.9 ± 5.0a
      Lower113.7 ± 7.9a78.8 ± 14.3a93.6 ± 15.9a76 ± 12.8b88.4 ± 25.6a58.1 ± 16.4b57.1 ± 16.6b63.1 ± 13.4a
      NMA
      (g·m−2)      		Upper37.8 ± 1.5a58.6 ± 4.7a69.6 ± 5.0a74.5 ± 5.7a77.9 ± 6.0a91.7 ± 6.1a83.9 ± 6.1a74.6 ± 6.5a
      Middle36.4 ± 2.2a51.2 ± 4.5b53.9 ± 4.6b59.4 ± 5.6b56.7 ± 5.1b66.9 ± 5.3b60.8 ± 5.4b56.2 ± 5.4b
      Lower29.1 ± 1.1a35.9 ± 5.0c40.4 ± 5.7c42.9 ± 5.6c39.6 ± 5.6c45.6 ± 5.5c44.1 ± 5.5c40.8 ± 5.5c
      rlwUpper14.2 ± 0.06a17.8 ± 0.10a18.6 ± 0.10a18.0 ± 0.08a17.7 ± 0.10a17.9 ± 0.11a17.4 ± 0.11a18.6 ± 0.12a
      Middle12.9 ± 0.07b15.7 ± 0.08b15.3 ± 0.08b15.2 ± 0.09b14.9 ± 0.08b14.4 ± 0.11b14.9 ± 0.09b16.2 ± 0.10b
      Lower11.5 ± 0.08c13.3 ± 0.11c13.3 ± 0.09c13.0 ± 0.10c13.5 ± 0.14c12.6 ± 0.10c12.9 ± 0.11c13.6 ± 0.11c
      Values are Mean ± SE. Mean values with same superscript do not differ significantly (p < 0.05).

      Seasonal variation of photosynthetic and morphological parameters of needles

    • All photosynthetic and morphological parameters differed significantly among the individual sample trees and fluctuated during the growing seasons (Fig. 2). Mean daily Amax 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 Amax ranged from 8.25 μmol·m−2·s−1 to 8.34 μmol·m−2·s−1. The mean daily RD 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). gs-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 WUEsat demonstrated a time course that is opposite to that of gs-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.

      Figure 2. 

      Seasonal evolution of (a) light-saturated net photosynthetic rate (Amax); (b) light-saturated stomatal conductance (gs-sat); (c) needle mass per area (NMA); (d) dark respiration (RD); (e) ratio of length to width of needles (rlw) and (f) light-saturated water use efficiency (WUEsat) for five sample trees. Data points represent seasonal mean values (solid bars represented stand error). Black solid line represented corrective mean values of five sample trees (a)−(f).

      Correlations between photosynthetic and meteorological parameters

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

      Figure 3. 

      Relationship between light-saturated net photosynthetic rate (Amax), light saturated stomatal conductance (gs-sat), dark respiration (RD), and light-saturated water use efficiency (WUEsat) and (a), (d), (g), (j) leaf temperature (Tleaf); (b), (e), (h), (k) relative humidity (RH); and (c), (f), (i), (l) vapor pressure deficit (VPD) for five trees.

      Relationships between physiological parameters and LMA

    • Amax and RD exhibited a positive and significant correlation with LMA (Fig. 4a & c), but there were slight differences in the correlation coefficients among the individual sample trees. Although gs-sat showed a similar correlation with LMA as Amax and RD, the correlation was weaker (r = 0.35, Fig. 4b). WUEsat only significantly correlated with LMA for two sample trees, even although a significantly negative relationship was observed for all the sample trees (Fig. 4d). WUEsat negatively correlated with LMA but was more significant seasonally than spatially.

      Figure 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.

    Discussion

      Spatial variation of needle physiology and morphology

    • Previous studies have suggested that light[15] and water potential[19,41] 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[42]. Recently, it has become increasingly accepted that the effects of these two factors vary with tree height[43]. 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[44]. Our results showed that Amax and gs-sat decreased significantly from crown top to bottom (Fig. 1a & c), which is consistent with that of other studies[45]. Martin et al. reported that shade leaves growing under less irradiance had lower leaf stomatal conductance than sun leaves[18]. RD negatively correlated with RDINC (Fig. 1b), as previously documented for different species[46], because leaves generally adapted to dark environments by reducing RD and Non-photochemical quenching. High leaf tissue density[15] decreased mesophyll conductivity to gas, and restrained RD[45], which was also proved by the higher positive correlation between RD and NMA (Fig. 4c). The vertical pattern of RD partly decreased Amax with increasing tree height but was not significant. NMA is one of the main morphological traits that changes in response to light variations[47]; thus, in our study, NMA followed the same pattern as Amax, gs-sat, and RD (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[48], leaf turgor pressure[45], 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 rlw 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 WUEsat originate from variations in photosynthetic rate, stomatal conductance, or both[49]. Studies showed that WUEsat negatively correlated with gs-sat[50]. In this study, WUEsat showed an opposite vertical tendency to gs-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[51].

      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[52]. Neglect of this variation may result in incorrect simulations of carbon uptake[53]. Previous studies revealed that Amax and gs-sat generally show a trend similar to a typical parabolic curve during the growing season[22,54] although contrary results have been reported[55]. However, Amax strongly correlated with gs-sat in the above investigations, indicating that stomatal behavior has a pronounced impact on Amax. Our results show that all the physiological and morphological parameters fluctuated during the growth season. Amax had a parabolic seasonal variation, similar to that reported in other studies[22] though accompanied by slight fluctuations in different trees (Fig. 2a), which was probably caused by the high correlation between Amax and seasonal variation of environment conditions (Fig. 3)[38]. Kunert et al. confirmed that short-term exposure to high temperatures poses a considerable threat to conifer species in Central European forest production systems[56]. During spring, an increase in Amax resulted from a gradual increase in photosynthetic capacity[37]. A decrease in Amax 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[57]. 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[28].

      Previous studies have suggested that seasonal variations in RD are mainly driven by seasonal patterns in temperature and NMA[58]. It is well known that starch and soluble sugars are the main reactants in the respiratory process; thus, their content directly affects respiration. Temperature also indirectly limits respiration by affecting the activity of enzymes that participate in respiration[59]. A previous study showed that a reduction in leaf expansion phase was due to a decrease in cytochrome-mediated respiration[21]. Our results implied that RD was significantly correlated with Tleaf (Fig. 3b) and NMA (Fig. 4b). The seasonal pattern of RD showed an obvious reduction in the leaf expansion phase and then slightly recovered with a little fluctuation (Fig. 2b), following a similar trend in other studies[60]. NMA showed a progressive increase throughout the growing season (Fig. 2e) owing to the accumulation of structural proteins and calcium. Seasonal variation in WUEsat was different from that in gs-sat (Fig. 2d), probably because WUEsat and gs-sat are negatively correlated[49]. Previous studies on seasonal patterns of leaf length, width, and thickness have shown that they universally follow a saturation or parabolic curve[58] throughout the growth season, reflecting the dynamic nature of photosynthetic acclimation[61]. We also observed variations in leaf shape and found that the ratio of length to width (rlw) 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 Amax, WUEsat and gs-sat were significantly different in the upper crown but not significant in the lower crown. Conversely, RD showed significant seasonal difference in the lower crown but not significant in the upper crown. Rare studys mentioned relevant results, but some research have proved that photosynthesis was more sensitive to light intensity and respiration was mainly affected by temperature[49−51]. 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, Amax, WUEsat and gs-sat showed different seasonal difference compared to RD.

      Table 4.  Results of the One-way ANOVA of photosynthetic parameters in each vertical layer.

      Vertical layerPhotosynthetic parameters
      AmaxRDWUEsatgs-sat
      1******
      2******
      3******
      4******
      5*
      6*
      7*
      8*
      9*
      The different parameters have been identified and described in the text. *, 0.01 < p ≤ 0.05; **, 0.001 < p ≤ 0.01.

      Relationship between needle physiology and environment

    • Tleaf showed a significant parabolic correlation with Amax (Fig. 3a), corroborating the results of many other studies that focused on different tree species such as, Quercus crispul[57], Picea mariana[62], Pinus cembra[63]. Both RH and VPD showed a significant relationship with Amax (Fig. 3e & i), 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[64] than that in other crowns. RD exhibited a typical exponential relationship with Tleaf (Fig. 3b), corroborating the results of other studies[46,58,62]. Both gs-sat and WUEsat significantly correlated with RH (Fig. 3g & h) and VPD (Fig. 3k & l), but the tendencies were diametrically opposite. Similar processes have been observed in other studies[37,50]. Some studies have suggested that variations in WUEsat across dates are primarily driven by gs-sat[49] and our investigation showed that WUEsat significantly correlated to gs-sat (r = −0.71). Thus, we suggest that the relationship among WUEsat, versus RH and VPD is likely caused by the influence of RH and VPD on gs-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[65]. Our results showed that Amax and gs-sat both had a significant positive correlation with NMA (Fig. 4a & c), similar to the results of previous studies on other species[66]. Other studies have shown a negative relationship between mass-based Amax and gs-sat versus NMA, probably because of the vertical pattern of NMA[44]. Han found a negative relationship between Amax and gs-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)[67]. Moreover, a NMA value of up to 500 g·m−2 for Pinus monticola [68], 800 g·m−2 for Sequoia sempervirens [19], and 1,000 g·m−2 for Pseudotsuga menziesii and Pinus ponderosa[68]; almost all these species are categorized as tall tree species with a negative relationship between Amax 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, Amax and RD is significantly and positively correlated with NMA, probably because of starch and soluble sugar contents[58]. However, WUEsat had a stronger correlation with RH and VPD than with NMA (Figs 3h, l & 4d), indicating that variations in WUEsat 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 the determination of optimal stand density. In addition, our results make it feasible to estimate the crown photosynthetic production and is helpful to further determine the contribution of branches to the trunk, which is the basis when making a pruning plan to produce no-knots wood and enhance the carbon sink capacity of young forests.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Liu Q, Li F, Xie L; data collection: Liu Q, Xie L; analysis and interpretation of results: Liu Q, Zhang Z, Xie L; draft manuscript preparation: Liu Q, Wang D, Zhang Z, Xie L. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

      Acknowledgments

      • This research was financially supported by the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China (Project# U21A20244), National Natural Science Foundation of the People's Republic of China (Project# 32201556), and Talent Introduction Research Project of Hebei Agricultural University (Project# YJ201942). We are deeply indebted to the academic staff, past and present postgraduate students of the Department of Forest Management, School of Forestry, Northeast Forestry University, who collected the data in the field.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (4)  Table (4) References (68)
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    Liu Q, Zhang Z, Wang D, Li F, Xie L. 2024. Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees. Forestry Research 4: e001 doi: 10.48130/fr-0023-0029
    Liu Q, Zhang Z, Wang D, Li F, Xie L. 2024. Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees. Forestry Research 4: e001 doi: 10.48130/fr-0023-0029
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  • Introduction
  • Materials and methods

  • Site description

  • Sample selection

  • Photosynthetic gas exchange measurements

  • Needle morphology measurements

  • Photosynthetic parameters

  • Statistical analysis

  • Results

  • Vertical profiles of photosynthetic and morphological parameters of needles

  • Seasonal variation of photosynthetic and morphological parameters of needles

  • Correlations between photosynthetic and meteorological parameters

  • Relationships between physiological parameters and LMA

  • Discussion

  • Spatial variation of needle physiology and morphology

  • Seasonal variation of needle physiology and morphology

  • Relationship between needle physiology and environment

  • Relationship between needle physiology and NMA

  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
  • About this article
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