Study Site and experimental design
The research was conducted during May-June 2023 on the territory of the Kornik Arboretum (Western Poland; 52.2448°N, 17.09698°E, 75 m a.s.l.); from 10:00 to 12:00 AM daily; ten 50×50 m experimental plots (EP l–10) were laid out. Five randomly located 1×1 m subplots were arranged within each EP (1–10), the total number of Q. robur and A. platanoides seedlings was tallied and the distance between individuals was measured. On each subplot, five seedlings of each of the two species were randomly selected and excavated for measuring leaf, stem, root and entire-plant traits. The seedling category of the species was based on three cotyledon characters of presumed ecological significance (position, texture and exposure) established by [56].
Indirect method, phytoindicative analysis
To collect the geobotanical data, five subplots 5×5 m (25 m2) were arranged within each EP (1-10). In each EP five subplots were located north, east, south, west and around a central point of EP. These subplots were not associated with 1×1 m subplots (2.1). Herb species identification to species level occurred in the field and was verified in a laboratory.
The phytoindication method was used to determine expert-based rankings of plant species according to their ecological optima on main environmental gradients [57-59]. The environmental indicators of properties of soil were identified using unified scales of environmental amplitudes [58, 60, 61]. The soil light (LC) is an important factor affecting the coenosis composition [58, 60]. LC takes values from 1 on heavily shaded soils to 9 on soils receiving full sunlight. The mean of indicator value was calculated as weighted averages based on the presence/absence of species on EPs.
Direct method, light measurements
Measurements of light availability (LAI and DIFN) with the LAI-2200 plant canopy analyzer (Li-Cor Inc., Lincoln, NE, USA) were taken. We measured leaf area index (LAI, m2 of leaves m−2 stand area) and DIFN (diffuse non-interceptance, the fraction of open canopy above the understory). For each experimental plot (EP 1-10) we took five series of twenty measurements at the height of 0.5 m above ground [62]. These five series were associated with collected geobotanical subplots. The measurements were collected during bright (clear) sky conditions. Light availability measurement was also recorded at a height of 10 cm above individual seedlings of Q. robur and A. platanoides on all subplots. For each individual seedling we took four measurements at a time.
Seedling above- and below-ground biomass
Tree seedlings were carefully rinsed and individually separated into fractions: leaves (with petioles), stems, and roots. All the samples were oven-dried at 65◦C (ULE 600 and UF450, Memmert GmbH + Co. KG, Germany) and weighed using BP 210 S (Sartorius, Göttingen, Germany) and Mettler Toledo PG 1003-S (Mettler Toledo, Columbus, Ohio, USA) scales (±0.001 g). A hand shovel was used to excavate the root systems, carefully sampling all the roots. Total seedling biomass (BIOM), stem biomass, leaf biomass, root biomass were measured. The trait mass fractions (App.) were calculated as (relative) dry mass per individual.
Leaf morphological characteristics
The leaf samples for morphological traits of tree seedlings were collected on the same day. The leaves per individual were harvested for both species put in plastic bags and transported to the lab. In the lab, all the samples were imaged and scanned using WinFOLIA 2013 PRO software (Regent Instruments Inc., Quebec, Canada). Based on the measurements, we calculated leaf mass area (LMA). We further calculated the leaf mass fractions (LMF), specific leaf area (SLA) and leaf area ratio (LAR) (App.). The leaf traits were calculated per individual plant [16, 63].
Stem morphological characteristics
The stem samples for morphological traits of tree seedlings were collected on the same day too. The stems per individual were harvested for both species put in plastic bags and transported to the lab. The length of stem was measured. The specific stem length (SSL), stem mass fraction (SMF) and stem root ratio (S/R) were calculated (App.).
Root morphological characteristics
The root samples of tree seedlings were thoroughly shaken to remove soil particles and placed in plastic bags. In the laboratory, root samples were soaked in water and carefully cleaned. Root morphological parameters, including the mean diameter, total surface area, root length, and volume were determined per plant. Roots of each order were scanned with a light transmitting desktop scanner in gray scale at 300 dpi. We used WinRHIZO software (Regent Instruments, Inc.). Specific root length (SRL), specific root area (SRA), root mass fraction (RMF), root length per unit plant biomass (RLPM), root length per unit leaf area (RLLA) were calculated [64]. Branching intensity of root (RBI) was calculated too [65] (App.).
Plasticity index
An index of phenotypic plasticity ranging from 0 to 1 was calculated for each variable and the two tree species as the difference between the minimum and the maximum mean values among the light treatments divided by the maximum mean value [66].
Community weighted mean
The CWM of each functional trait was calculated for each 1×1 m subplot as the sum of the product of the trait values (above-ground and below-ground traits) of both species by its relative abundance [67].
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where n is the species number of a seedling plot; Pi is the relative abundance of species (total abundance in each seedling plot was used), and traiti is the trait value of each species.
Statistical analysis
Summary statistics were calculated including mean, median, min, max, standard deviation, standard error and variability (coefficient of variation) for A. platanoides and Q. robur seedling traits (BIOM, LA, LMA, LMF, LAR, SLA, SSL, SMR, S/R, RMF, SRA, SRL, RLPM, RLLA, RBI, Table 1). Linear regression analysis was used to predict the value of LAI on the values of leaf, stem, and root functional traits the individual level. Linear regression analysis was used to predict the values of LAI, DIFN, LC on the value of community-weighted mean leaf, stem, and root traits. We used a plot correlation matrix (Pearson, p<0.05, p<0.01, p<0.001) to examine the internal and external relationships between functional traits: leaf, stem and root. We conducted a principal component analysis (PCA) to relate the variations of indices of functional traits to the determined light factors. For the analytical processing of the field and laboratory data, the calculation was performed using OriginPro 2022 software.