DOI: 10.5061/dryad.3bk3j9m0c
title: Contrasting light demands determine the coordination of plants’ non-structural carbohydrates and economic strategy over the range of solar spectral composition

description: Non-structural carbohydrates (NSC) are critical mediators of plant
 adaptation to fluctuating light environments. They are tightly coordinated
 with plant economic strategy, i.e., leaf economics spectrum (LES) and root
 economics spectrum (RES). However, the role of solar spectral composition
 in shaping the NSC pool of tree species and their relationships with LES
 and RES is poorly understood. We examined plant economic traits and NSC of
 two functional groups, light-demanding vs shade-tolerant tree seedlings,
 grown under five spectral-attenuation treatments: 1) control, transmitting
 95% of solar radiation (CK); 2) attenuating ultraviolet (UV)-B radiation;
 3) attenuating all UV radiation; 4) attenuating all UV radiation and blue
 light; 5) attenuating all UV, blue, and green light. Short-wavelength
 regions (UV) had strong effects on plant economic traits and the NSC
 dynamic, irrespective of functional groups. Shade-tolerant species
 exhibited higher trait plasticity than light-demanding species.
 Coordination of NSC and economic traits differed between the two groups.
 Across treatments, leaf and root NSC were negatively correlated with LES
 for both groups, and with RES for light-demanding species, while they were
 positively correlated with RES for shade-tolerant species. A more tightly
 coordinated trait-NSC network was evident in light-demanding species, and
 UV-A radiation promoted the network tightness. These findings highlight
 the role of spectral composition in regulating the coordination between
 above-/belowground functional traits and C dynamics. Different tree
 species may have employed contrasting strategies to adapt to the solar
 spectral composition in their habitats.
description: Linear mixed effect models (LME) were used to explore the effects
 of spectral treatment and functional group, and their interaction on each
 functional trait (Table S3), with species and blocks as random factors,
 using the <em>nlme</em> package (Wang et al., 2020). Pair-wise
 comparisons were conducted to analyze the effects of specific spectral
 treatments with the <em>multcomp</em> package (Hothorn et al.,
 2008) when the effect was significant (<em>P</em> &lt;
 0.05). <em>P</em>-values were corrected with the
 Benjamini-Hochberg (BH) method (Benjamini and Hochberg, 1995). We used
 redundancy analysis (RDA) to assess the relationship between spectral
 treatments (using the actual irradiance measured under the filters) and
 leaf/root traits for the two studied functional groups (light-demanding
 and shade-tolerant), using the <em>vegan</em> package (Oksanen
 et al., 2015). Statistical significance was determined by the Monte Carlo
 permutation method and Bonferroni’s test (permutations = 999,
 <em>P</em> &lt; 0.05). Prior to analysis, data were
 log-transformed, and response variables (functional traits and NSC
 variables) were centered and standardized. The RDA provides a
 visualization of the primary gradients in the trait-spectra relationships,
 while the formal statistical tests for the independent effects of each
 spectral region are presented in subsequent analyses.
 Relative Distances Plasticity Index (RDPI) was used to determine
 the plasticity of functional traits to each spectral region (Valladares et
 al., 2006; Ma et al., 2024). RDPI was calculated as the Euclidean distance
 (<em>d</em>) between the trait values exposed to two spectral
 regions (ij and i′j′, respectively), normalized using the sum of the
 absolute trait values (x<sub>i′ j′</sub> +
 x<sub>ij</sub>). Then, LME were used to explore the effect of
 plant traits, spectral region, and their interaction on RDPI, with species
 and traits as random factors (Wang et al., 2020). Differences in plant
 trait plasticity between functional groups (light-demanding
 <em>vs</em> shade-tolerance) across treatments were tested
 using <em>t</em>-test. Principal component
 analysis (PCA) was conducted to explore leaf and root trait coordination
 across spectral treatments, related to resource acquisition strategies for
 each functional group, using the FactoMineR package (Husson et al., 2014).
 The scores from the first PCA axis (PC1) represented the continuum of the
 LES (LN, LPhen, SLA, and LCN). The scores of the first two axes of root
 traits were also selected, with the former dominated by RN and RTD, and
 the latter dominated by RD and SRL, representing root conservation and
 foraging and dimensions, respectively. Linear regressions were performed
 to explore the relationships between PC1 and PC2 scores of LES and RES and
 concentrations of NSC and its components in leaves and roots,
 respectively, for each spectral region. Plant trait
 networks were constructed to examine the coordination between key economic
 traits and NSC across spectral treatments and functional groups. This was
 based on a Pearson adjacency matrix with a significance threshold of
 <em>P</em> &lt; 0.01 (e.g., |r| ≥ 0.2 at
 <em>n</em> = 75) (Csardi & Nepusz, 2006). In the
 network, plant traits and NSC variables were represented as nodes, while
 trait–trait relationships were represented as edges. Network connectivity
 and centrality were calculated for each trait and NSC variable using the
 <em>degree</em> and <em>betweenness</em> functions
 in <em>IGRAPH</em> (Csardi & Nepusz,
 2006)<em>.</em> Three parameters [edge density (ED), diameter
 (D), and average path length (AL)] were used to quantify the tightness,
 and the average clustering coefficient (AC) was used to quantify the
 complexity (He et al., 2020). Networks with higher D and AL exhibit
 greater overall independence among traits, whereas those with higher AC
 are more extensively partitioned into several distinct components
 (Armbruster et al., 2014; He et al., 2020). All statistical analyses were
 performed in R version 4.3.3 (R Core Team, 2024).
description: # Contrasting light demands determine the coordination of plants’
 non-structural carbohydrates and economic strategy over the range of solar
 spectral composition Dataset DOI:
 [10.5061/dryad.3bk3j9m0c](https://doi.org/10.5061/dryad.3bk3j9m0c) ##
 Description of the data and file structure The data file contains one
 dataset: the concentrations of non-structural carbohydrates (NSC,
 including starch and soluble sugars) in roots and leaves, and the
 functional traits in leaves (four variables) and roots (six variables) of
 six tree species (three light-demanding and three shade-tolerant species)
 across five spectral treatments. ### Files and variables #### File:
 data.xlsx **Description:** The data includes the concentrations of
 non-structural carbohydrates (NSC, including starch and soluble sugars) in
 roots and leaves, along with their functional traits. *Species*: Tree
 species used in this study, including three light-demanding (*Betula
 platyphylla*, *Quercus mongolica*, *Tilia amurensis* ) and three
 shade-tolerant species (*Acer pseudosieboldianum*, *Acer pictum, Pinus
 koraiensis*) *Functional group*: These six species differ in their
 ecological niches and growth, and can be categorized into light-demanding
 species and shade-tolerant species. *Spectrum*: Spectral treatments, here,
 five optical filters made using films of defined optical transmittance
 were mounted on frames to create five spectral treatments: 1)
 Full-spectrum (control, &gt; 280 nm), using a fully transparent
 polyethylene film to transmit approximately 95% of the solar spectrum
 above 280 nm; 2) No-UV-B (&gt; 315 nm), utilizing a polyester film to
 attenuate wavelengths below 315 nm; 3) No-UV (&gt; 400 nm), employing a
 polyester filter to attenuate wavelengths below 400 nm; 4) No-UV/Blue
 (&gt; 500 nm), using a polyester filter to attenuate wavelengths below 500
 nm; 5) No-UV/BG (blue and green light) (&gt; 580 nm), using a polyester
 filter to attenuate wavelengths below 580 nm. *Leaf /Root non-structural
 carbohydrates (NSC) (mg g ^-1^):* NSC includes soluble sugars and starch,
 and the NSC concentration is the sum of both. *Leaf functional traits:* 1)
 Leaf carbon-to-nitrogen ratio: the ratio of carbon to nitrogen
 concentration in leaves. 2) Specific leaf area (cm^2^ g ^-1^): leaf area
 per mass. 3) Leaf nitrogen concentration (mg g ^-1^): nitrogen
 concentration in leaves. 4) Leaf total phenolic concentration (mg g
 ^-1^): total phenolic concentration in leaves. *Root functional traits*:
 1) Root nitrogen concentraion (mg g ^-1^): nitrogen concentraion in roots.
 2) Root diameter (mm). 3) Specific root length (cm g ^-1^): root total
 length per mass. 4) Root carbon-to-nitrogen ratio: the ratio of carbon to
 nitrogen concentration in roots. 5) Root exudation rate (mg g ^-1^ h
 ^-1^): the collected dissolved organic carbon content of the root
 exudation by the dry weight of collected roots over time. 6) Root tissue
 density (g cm ^-3^): root tissue density was obtained as dry mass divided
 by total volume. Variables: Non-structural carbohydrates (NSC, including
 starch and soluble sugars) in roots and leaves. Four leaf functional
 traits are specific leaf area (SLA), leaf nitrogen concentration (LN),
 leaf carbon-to-nitrogen ratios (LCN), and leaf total phenolic
 concentration (LPhen); Six root functional traits are root diameter (RD),
 specific root length (SRL), root tissue density (RTD), root N
 concentrations (RN), root C: N (RCN), and root exudation rate (RER). ##
 Code/software R version 4.3.3 (R Core Team, 2024). ## Access information
 NA