The resource strategy of seedlings is an important key for understanding the adaptation of trees at this ontogenetic phase to abiotic changes. In this study, we tried to determine the patterns of response of functional traits of shade-tolerant and shade-intolerant species along natural environmental light gradients, using an entire-level, trait-based approach by direct and indirect methods in the Arboretum at Kórnik (Poland). Differences between the two species were found for some variables. Analysis of phenotypic plasticity indices of leaf, stem and root traits of seedlings had high values for both species. The values of plasticity indices of A. platanoides root traits were lower compared to the corresponding traits for Q. robur. Relationships between measures obtained from individual-level trait data were stronger than relationships with measures obtained from community-level trait data. The data obtained from the direct method revealed the closest relationships between functional traits of seedlings and light changes at the individual level trait data for both species. Correlation links between LAI and leaf (LMA, SLA) and stem (SSL, SMR) traits were less tight for Q. robur compared to A. platanoides. The indirect phytoindicative method revealed relationships between LC and LMA, and S/R of seedlings based on community-level trait data. Close relationships between LC and LMF, and SLA were not established in contrast to LAI and DIFN. Internal (closest) and external links between the leaf, stem, and root functional traits at the community-level trait data were established.
Research Article
The Impact of Light Availibility on the Functional Traits of Quercus Robur L. And Acer Platanoides L. First-year Seedlings by Direct and Indirect Methods
https://doi.org/10.21203/rs.3.rs-4289405/v1
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seedlings
oak
maple
light availability
traits
plasticity
The link between functional characteristics of plants and their habitats has always been an important component of plant science [1-7]. Plant functional trait-based research spanning multiple levels of biological organization from cells and tissues, to individual organisms and populations, to communities and ecosystems, are an actual and important tool for studying many ecological issues [8-14]. It is common knowledge that plant economics spectrum theory attempts to integrate leaf, stem, and root traits to explain plant ecological strategies [5, 8, 15-17]. The theory predicts that the traits are correlated with each other and coordinated across different organs, and functional traits should have adaptive significance along environmental gradients in temperate broadleaf forests [2], subarctic flora [18] and Mediterranean rangeland [19]. The leaf, stem, and root traits should respond to environmental gradients (particularly light) [20-24]. Economic traits of plants influence performance and fitness consistent with trait-based theory about underlying adaptive mechanisms [23]. However, some studies did not support consistent coordination between above-ground and below-ground traits. Root traits mirrored stem traits rather than leaf traits in a Neotropical region [9]. The leaf and stem economic spectra were not only independent [17], but also orthogonal [24], suggesting that functional trade-offs operate independently at the leaf and stem levels [25]. Many root, stem, and leaf traits of plants were coordinated, but at the species level, root traits were not clearly related to growth rate and loaded on a separate principal component from the plant economic spectrum [20]. Some studies show that roots are subject to a more complex environment than above-ground traits [26]. Above-ground traits cannot adequately capture the variety of below-ground functions and trade-offs that drive differences in plant performance across species [20, 22, 27]. The relationships between leaf and root traits can shift depending on soil properties [28], water availability [29], and other abiotic factors [22]. Consequently, some functional plant trait variations might be uncorrelated due to ecological variabilities in above- and below-ground environments [30].
The study of the morphological/ecological characteristics and functional traits of tree seedlings is of keen interest among recent ecological submissions. The seedling stage of trees is particularly critical because survival and performance at this ontogenetic phase of tree development will characterize the future of forest/park ecosystems [31]. All the same, seedling functional types refer to morphology of seedlings in relation to cotyledon function and position. It is a categorical trait that can be used to characterise plant ecological adaptations [32]. Seedling survival of trees of different functional types has an imprint on seedling and adult community structure. This ecological aspect requires knowledge regarding how individuals interrelate with habitat. Seedling functional traits enable us to quantify strategies for different environments [33, 34]. A number of functional traits contribute to seedling persistence in the forest understorey [35, 36]. Most seedling traits depend on the environments, either marginally changing (weak trait × environment interaction) or reversing (strong trait × environment interaction) along abiotic gradients [37].
Intensity of light is a key parameter for seedling development, and thus it is capable of influencing forest composition, structure and dynamics [38, 39]. Survival of seedlings is closely correlated with seed mass in low light, whereas the relationship becomes insignificant in intensive light [40]. At low irradiance, species deviate most in their morphological characteristics because light interception is crucial in a light-limited environment. At high irradiance species deviate most in their physiological characteristics, because high irradiance allows the species to realise their full photosynthetic capacity and growth potential [21, 32, 41, 42]. Several studies have documented the large variation displayed by these traits under different light regimes, along with their effects on survival and plant growth [35, 42, 43]. Low light availability in the understorey of natural communities primarily results from overlying plant canopies and the potential occurrence of differently sized gaps [13, 44]. Seedlings of shade-tolerant and shade-intolerant species have high survivorship in the shade cast by conspecific adults. However, only some of the shade-tolerant species have low rates of sapling mortality under the low light levels characteristic of stands dominated by late-successional species [44].
It is also important to understand the light requirements of coexisting species during different stages of development. This trait varies among coexisting species, showing a continuum of seedling performance strategies [14, 45] and the position along the growth-survival trade-off [46]. Growth rates for different-light survivorship strategies are inversely correlated across coexisting species [45]. Seedlings of shade-intolerant species occurred at significantly higher light levels than the shade-tolerant species. The proportion of seedlings in low-light conditions was negatively correlated with the successional position of the species [47]. The differences in light use between strategies are invoked to explain seedling structure and performance through optimum growth and survival relationships. Shade-tolerant and shade-intolerant species therefore differ in a predictable way in their seedling traits. The species face a trade-off between a high survival rate in the deep shade, and a high growth rate in high light conditions [35].
Phenotypic plasticity of plants in different stages of development is essential for survival in heterogeneous and variable environments [48]. A new approach demonstrated that the plasticity in some plant traits may represent specialization [49]. The plastic response for morphological parameters to environmental deterioration is adaptive only if the alternatives are dormancy or death and is generally less adaptive than phenotypic stability [48]. The study of phenotypic plasticity of morphological parameters of leaves, stems and roots of shade-tolerant and shade-intolerant seedlings is especially relevant in changing environmental conditions.
The plant ecological spectrum/phenotypic plasticity and seedling traits are coordinated, but to what extent does the strength of this link vary with the different light availabilities? Shade-tolerant species grow slowly and have higher survival rates, whereas shade-intolerant species grow quickly but have lower survival rates [50]. The functional traits should be predictors of demographic rates, but results in the literature are mixed indicating that additional information regarding organismal function should be considered. Most of the scientific research literature on this topic was carried out in natural tropical and temperate forests. However, information regarding conducting research under in situ conditions is not sufficiently demonstrated. This aspect is very important for understanding the conservation and reproduction of plant biodiversity.
Acer platanoides L. (a shade-tolerant species) is the most widespread native maple in Europe and is generally found in small groups or as scattered individuals in mixed forests [51]. Quercus robur L. (a shade-intolerant species) is also among the most important tree species in Central Europe, both from ecological and economical points of view [52]. A. platanoides and Q. robur are frequently noted seedlings in forests in Poland [53]. Besides, the maple [54] and oak [55] seedlings can grow under a canopy of other trees. The plant economics spectrum shows that ecological traits of seedlings of both species are functionally coordinated along a light gradient. However, empirical evidence is mixed about whether above-ground and below-ground traits are consistently linked. We expect links of light parameters and a number of functional traits of the established seedling phase of A. platanoides and Q. robur.
We predict that: (1) changing light availability (direct and indirect methods) and functional parameters of seedlings (leaves, stems, roots) of shade-tolerant and shade-intolerant species at the individual and CWM levels are correlated; 2) variation exists in the phenotypic plasticity of functional parameters of seedlings and local environments; 3) internal and external parameters of the functional characteristics of maple and oak seedlings are correlated.
The current study aimed to investigate functional traits of first-year seedlings of two key tree species of Poland in response to light availability.
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].
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.
Diagnostic and dominant species in the herbaceous layer on the EPs in the Arboretum were Bromus sterilis (L.) Nevski, Convallaria majalis L., Impatiens parviflora DC., Elytrigia repens (L.), Geum urbanum L., Geranium robertianum L., Melica uniflora Retz., Galeobdolon luteum Huds. and Galium odoratum (L.) Scop. The mean foliage cover was 75-85% on EPs. The main canopy trees species were A. platanoides, Q. robur, Carpinus betulus L. and Abies alba Mill.
Development of first-year seedlings only of A. platanoides and Q. robur were detected on the EPs. The functional types established were phanerocotylar epigeal with foliaceous cotyledons (PEF) for A. platanoides and cryptocotylar hypogeal with reserve storage cotyledons (CHR) for Q. robur (Fig. 1).
Comparative analysis of leaf, stem and root traits/plasticity indices of tree seedlings
The calculated values of functional traits of A. platanoides and Q. robur seedlings on the EPs differed in ways that reflected the established functional types of the seedlings (Table 1). The obtained mean/median values of the studied traits corresponded to the ontogenetic stage of development of seedlings and species for forests of the temperate zone. The coefficient of variation of BIOM was higher for maple (62.51%) than for oak (52.21%). LA (45.43%) and LAR (29.85%) had the maximum variability among the leaf functional traits of A. platanoides. LMA and LMF of maple had almost the same variability. The maximum values of variation coefficient among leaf functional traits of Q. robur was for LA and LAR by analogy with A. platanoides. However, the variation coefficient of LMA was only 18.63% for oak seedlings. It is worth noting the different variability of SLA for the studied seedlings: almost twice higher for maple than for oak.
High variabilities of stem traits (29.16%–69.81%) and root traits (30.34%–67.45%) were found for seedlings of both species. There was no significant difference in the variability of stem traits for seedlings of A. platanoides and Q. robur. The maximum value of this parameter was detected for RLLA among root traits: 50.31% (A. platanoides) and 67.45% (Q. robur). Values of RMF and SRA were similar for both species. The variability of RLPM was slightly higher for oak than for maple.
Plasticity indices of the studied parameters of leaves, stems and roots were high for seedlings of both species (Fig. 2). The maximum values of ecological plasticity among leaf traits were found for LAR and LMF. Moreover, the SLA plasticity index for Q. robur was slightly more than 0.5, while the index was 0.73 for A. platanoides. This was consistent with our previous data. Among stem parameters studied, S/R had the highest plasticity for both species. RLLA (Q. robur) had the maximum value of plasticity among root functional traits. RBI of oak seedlings had the minimum value of plasticity. Generally, the root functional traits of A. platanoides seedlings had lower values of plasticity indices compared to the corresponding parameters for Q. robur. The dissimilarity (∆) between the studied traits was the largest for SLA (-0.18), RBI (-0.19), and SMR (0.16). The minimum (∆) was recorded for LAR (0.03), S/R (0.04), SRL (0.03), and RLPM (0.05) (Fig. 2).
The relationships between light availability and functional traits at the individual level by direct methods
The relationships between the functional traits of shade-tolerant and shade-intolerant species (individual level of analysis) and light availability in the studied environmental conditions of the Arboretum showed similar results for A. platanoides and Q. robur seedlings, but with certain differences (Fig. 3, 4). The particular contrast of seedlings of both species was between the investigated light conditions and functional traits of stem and root (Fig. 3b, Fig. 4b). The studied morphological leaf traits differed in value per species but were similarly and significantly modified by light levels.
For A. platanoides, variables SLA/LMA/LAR had significant positive relationships with LAI, while LMF had moderate negative trends in the relationships (Fig. 3a). Instead, variables SSL/SMR had positive relationships with LAI and S/R had non-significant negative trends in the relationships in this study (Fig. 3b). The obtained results regarding the impact of light availabilities on the root traits of A. platanoides were quite expected (Fig. 3c). Variables RMF/SRA/SRL/RLPM/RLLA had non-significant positive relationships with LAI. A moderate relationship was established between LAI and RBI (R2=0.15, r=0.39, P<0.01). The relationships between LAI and SRA/SRL were almost the same strength.
The relationships between the functional traits of oak seedlings and the change in light availability revealed trends similar to maple seedlings. However, some differences in relationships were recorded. For Q. robur, variables SLA/LMA/LAR had significant positive relationships with LAI (Fig. 4a). However, the relationship between LAI and LAR for Q. robur was closer (R2=0.23, r=0.49, P<0.0001) compared to A. platanoides. The relationships between LAI and LMA/SLA were weaker for the shade-intolerant species than the shade-tolerant species under the light conditions studied. The variable SSL had significant negative relationships with LAI and SMR, S/R had non-significant positive trends in the relationships in this study (Fig. 4b). The negative relationship between SSL and LAI (R2=0.12, r=-0.34, P<0.01) for Q. robur seedlings contrasted with the positive relationship for A. platanoides (R2=0.17, r=0.41, P<0.001). The correlation between LAI and SMR was half as large for oak as compared to maple. The S/R parameter of Q. robur seedlings had the opposite trend compared to A. platanoides seedlings (Fig. 3b, Fig. 4b). The variables RMF/SRA/SRL/RLPM/RLLA had non-significant positive or negative relationships with LAI. The links between the root traits of Q. robur seedlings and the studied light availability in the Arboretum were predictable (Fig. 4c). These root links were substantially weaker than for A. platanoides. The relationship between LAI and SRL (R2=0.04, r=-0.20, P<0.05) was the most closely related.
The relationships between light availability and community-weighted mean functional trait values by direct and indirect methods
The analysis at the community-weighted level (absolute abundance of the studied species) on the EPs had results that differed from the individual level (3.2). The relationships between functional traits of leaves and light changes were not significant at the community-weighed level. (Fig. 5). The functional traits of seedling leaves were weakly correlated with DIFN, LAI (direct analysis) and LC (phytoindicative analysis) (Fig. 5). The established relationships were weakly positive or negative. Opposite low correlations between LAI and SLA/LAR; DIFN and SLA/LAR were detected. The correlation between DIFN and SLA (Fig. 5a) was low compared to LAI and SLA (Fig. 5b). The relationships between LAI/LMA and DIFN/LMA had almost the same strength. The LC parameter revealed negative correlations with LMA and LAR. The relationship between LMA and LC was established only by indirect phytoindicative analysis (Fig. 5c). LC was not associated with LMF and SLA in contrast to LAI and DIFN.
The analysis between stem traits and studied parameters of light at the community-weighted level revealed non-significant relationships of different strengths and directions (Fig. 6). Positive correlations between SSL and LAI; SMR and LAI were established (Fig. 6b). The strength of the relationship between the corresponding stem traits and LAI was closer compared to the corresponding data for DIFN. The relationship between LC and S/R had a significant negative correlation (R2=0.22, r=-0.47, P<0.05) (Fig. 6c) while correlations with LAI and DIFN were not significant (Fig. 6a, b). LC and SSL/SMR relationships were quite weak. Assessment of the relationships between root traits and light availability at the community-weighted level by direct analysis in the Arboretum territory did not detect significant relationships (Fig. 7a, b). The maximum strength of correlations was found for: DIFN and RLLA, LAI and RMF, LAI and RBI. Less close relationships were found for root traits and light parameters compared to leaf/stem traits at this level of analysis. However, negative correlations between LC and RBI (R2=0.25, r=-0.51), SRA (R2=0.11, r=-0.30) and SRL (R2=0.09, r=-0.29) were detected (Fig. 7с).
Light availability and seedling traits
To understand the multidimensional relationships between the light factors considered in this study, we conducted a principal component analysis (PCA). The РСА illustrated the indices of functional trait responses to the light factors by direct and indirect methods (Fig. 8). Vectors for S/R, LMF, SRL, SRA, SMR, had similar directions and lengths and were positively associated with scores on PC1. The main traits affecting PC1 were LAR, RLPM, and SRA, in a positive direction and LA, BIOM, and RLLA in the negative direction. This corresponds to Acer (positive) and Quercus (negative) for PC1. This clustering is represented by the sites evaluated on A. platanoides. Q. robur showed lower PCA1 scores than A. platanoides. RLLA and RMF were the least associated with the two main components according to the conducted PCA. The leaf traits were the most associated with the two main components. We can conclude that LAR, LMF, SLA, SRL, SRA were the most important for PC1. Similarly, we can state that LC and RBI were the most important for PC2.
Interrelationships of community-weighted mean seedling traits
An assessment of correlations between the community-weighted mean functional traits of seedlings was carried out in the studied EPs (Fig. 9). Internal correlations between leaf, stem, and root traits were quite expected. External correlations between some traits of seedlings were completely unexpected. In particular, strong positive correlations between LMF and RLPM (0.71); LMF and SRL (0.76); LMF and SRA (0.73) were established (Fig. 9). Very strong relationships between LAR and RLPM (0.78); LAR and SRL (0.81); LAR and SRA (0.80) were also detected. The highest correlations among stem/leaf traits relationships were found between LAR and SMR (0.85); LMF and SMR (0.78); SLA and SSL (0.82). It should be noted that SLA was not closely related to other stem and root traits. The relationships between SSL and LMA/LAR were similar in strength and opposite in direction. Strong positive correlations were also found between SMR and RLPM/SRL/SRA (0.74, 0.82 and 0.81, respectively).
The contrasting phases of tree growth from seedlings to mature plants and different plant organs (i.e., leaf, stem, and roots) are priority areas for the study of forest ecology [33, 34]. Interspecific variation in demographic rates are more important for small plants than for large trees, which makes it statistically easier to detect trait–abiotic factor relationships. Seedling functional type is a categorical trait that can be used to characterise plant regenerative strategies and plant adaptation to environmental changes [32]. Most of the studies have been carried out on first-year tree seedlings with phanerocotylar epigeal germination with foliaceous cotyledons and cryptocotylar hypogeal germination with reserve storage cotyledons [31]. Traits of these seedling functional types influence growth, reproduction, and survival of seedlings in the future [6, 10, 34] and are very important indices for understanding how seedlings respond and adapt to changing light conditions [32, 33]. The different directions and strengths of the relationship patterns of functional traits were characterized by previous studies [9]. Traits of leaf, stem and root delineated two orthogonal axes of functional trade-offs: a first axis defined by leaf traits, corresponding to a «leaf economics spectrum», and a second axis defined by covarying stem and woody root traits, corresponding to a «wood economics spectrum». These axes remained consistent when accounting for species evolutionary history with phylogenetically independent contrasts [9].
Light is a limiting factor for seedlings [68]. The seedling morphological response to availability of light could be a major key that differentiates ecological niches of species. Seedling spatial distribution was significantly associated with light. Light heterogeneity also promoted seedling growth, but had no discernible effects on seedling survival [69]. Previous results showed that seedlings of temperate and tropical plants are limited by light in shaded habitats [50, 70] and variation in functional traits of trees are expected to help capture the variation in light [15, 40, 71]. The plant economics spectrum in the seedling community suggests that limitation by canopy openness is not the major factor driving the functional response in forest ecosystems [26]. The survival rates of shade-tolerant species will not be as strongly influenced by light. Shade-intolerant species, however, do not tolerate low light levels [50]. Seedlings of the different species differentially allocate biomass to stems, leaves and roots in response to differences in light levels [72]. Plant biomass increased and shoot–root ratio decreased with light availability. The species–treatment interaction was significant when plant biomass was taken as a covariate, revealing significant species differences in their plastic phenotypic response to light, that were not simply due to differences in plant size [66].
The results obtained in this study indicate a slight variation in the values of biomass, as well as the leaf/root functional traits of shade-tolerant and shade-intolerant seedlings under different light availabilities. Correlations between seedling above-ground biomass and morphological traits are strongest at low irradiance where light interception is important [73]. Conversely, correlations between seedling mass and physiological traits are strongest at high irradiance, where maximisation of photosynthetic rates is important [40]. The correlated links between studied leaf traits of shade-tolerant and shade-intolerant species and light availabilities at the individual level of analysis were shown in this study. The relationships between studied root/stem traits of both species and light availabilities at the individual level of analysis were weaker in comparison to leaf traits. The obtained negative and positive relationships between various functional traits of first-year maple and oak seedlings were related to the stability of the development of individuals in the studied abiotic conditions. We did not measure temporal heterogeneity of light, since we only measured canopy openness in one growing season (2023). Therefore, we agree with authors [26] that unidentified temporal variation in canopy openness could also contribute to the link between seedling traits and the light environment.
Shade-intolerant species were characterised by a low share of their saplings in low-light conditions, fast height growth and a strong growth response to light [21, 47]. The first-year seedlings of shade-intolerant species have high population-level mortality rates in the shaded understory [17]. The seedlings of shade-intolerant species responded to the shading with reduced above-ground biomass. The effects of shading on growth and/or morphology were expected to differ between the shade-tolerant species and the shade-intolerant species [74]. The similarity in species responses leads to the conclusion that under experimental conditions species display a similar tolerance to shade. The differences in the effects of shading on seedling size and biomass distribution might first become evident in the sapling stage. When resources are limited, shade-intolerant species can draw upon the large reserves of energy in their cotyledons for their first year of growth and development [75]. This conclusion was confirmed in our study too. Other studies found that shaded seedlings had a proportionally higher SLA and stem biomass, and a smaller below ground biomass. Seedlings put more emphasis on shoot growth than root growth, which is a common response of tree seedlings to variation of light intensity (morphological adaptations) [74]. Studies with seedlings invariably show that SLA is an important predictor of interspecific variation in growth and survival [21, 35, 40, 76]. Leaves of those seedlings growing in poorly lit conditions had a higher SLA, and a higher biomass allocation to stems and leaves relative to root allocation, compared to those growing in less limited light conditions [72]. Some studies have not observed strong correlations between leaf/root economic traits and showed higher complexity of environmental constraints and plant functions below ground than above ground [77]. Our results are consistent with studies that have found coordination between leaves and roots [18, 19]. SRL has been viewed as a below-ground analogue to SLA, where high SRL might facilitate faster growth through more rapid acquisition of soil resources [20]. Substantial trait coordination occurred between leaves and roots, but the strength varied between growth forms and clades [22]. The slope between LMF, and RMF shows no relationship with irradiance, whereas the slope of SMF versus seed mass increases with irradiance [40]. Higher allocation to roots can occur early in ontogeny [78]. The plasticity of S/R ratios can be a function of different environmental factors [79], or even ecotypes with different strategies of resource uptake [80]. A statistical relationship between S/R and light parameters measured by direct methods was not detected in this study, in contrast to the relationship with data obtained by indirect methods. Seedlings increased horizontal growth relative to vertical growth in environments with greater irradiance. Root mass ratio increased with increasing irradiance in shade-intolerant and shade-tolerant species and was not different between the species [43, 81]. Across the larger environmental gradient, leaf and root functional traits were significantly correlated [17]. Positive correlations between leaf traits and root traits were also established in this study (LMF and RLPM, LMF and SRL, LMF and SRA, etc.). This conclusion indicates an intrinsic trade-off between the resource allocation into more efficient absorbing surfaces of either leaves or roots [25]. This study confirms the hypothesis that root traits are more than analogues of leaf traits within a plant economics spectrum. The results reveal a novel ecological pattern and highlight the power of root data to close important knowledge gaps in trait-based plant ecology [82].
A. platanoides seedlings had higher densities, recruitment, and survival than Q. robur [83]. Biomass allocation between different compartments was also dependent on seedling size. With increasing intensity of light, A. platanoides maintained a constant proportion of biomass in foliage, but the relative amount of foliage decreased in Q. robur. As a result of these interspecific differences in fractional allocation of sapling biomass in foliage and biomass requirement for construction of foliar surface area, LAR was larger in A. platanoides. Q. robur had a larger SMR [84, 85] and SLA in the lowest light levels [72], while A. platanoides had a greater LMR/LMA [63, 81]. Although shade treatments significantly affected oak seedling growth, they did not influence seedling survival. The biomass increased as light intensity increased. Oak seedlings showed typical acclimation to shade [85], including greater SLA and lower leaf thickness and root:shoot ratios with increasing shade [36]. The results of another study suggested that Q. robur seedlings would have high survival rates and would acclimate well if underplanted below overstories that reduced the available light to as low as 28% of full light [86]. Positive correlations between LAI and SLA, and LAI and LAR, of first-year oak seedlings were detected in our study. The data obtained in this study regarding the response of functional traits of A. platanoides to light availability are consistent with the results of other authors. A. platanoides responds to even small increments in light intensity because its foliage characteristics allow more efficient light capture [54]. We confirmed that slightly increased light caused a change in the values of morphological parameters of leaf traits of A. platanoides. We agree with the conclusion that due to a decreasing investment of resources in foliage construction with advancing seedling ontogeny, first-year seedlings require more light to survive in Q. robur than in A. platanoides [54].
These results indicate that interspecific differences significantly alter species competitive relationships during seedling development in ex situ. These species differences in their functional response to light reinforce the view of a different regeneration niche for each of the two species studied. The different response of functional traits of both species to light changes could stem from the fact that shade-intolerant species with traits adapted for fast resource acquisition (e.g., high LAR, SSL, SRA) were more abundant in the seedling community [87]. Greater investment of biomass in leaves vs. standing biomass may result in lower volume gain and reduced competitive ability in more open habitats in A. platanoides. This is in contrast to Q. robur, which not only tolerates full sunlight but also requires increased irradiance after the first year of emergence for successful regeneration [85].
Ecological assessment based on only a specific biomass component is potentially misleading because plasticity differs across the complete dose–response continuum and are further driven by a series of factors. Seedling trait estimates should be based on several components [88]. A multifaceted approach was also used in our study. Plasticity occurs in the context of optimal partitioning theory, however, it is constrained by plant ontogeny [78]. Trait plasticity is an important factor affecting upscaling from individual to community-level biomass productivity. The establishment of the function–performance relationships that should lead to more robust predictions is a debatable issue. The issue of the value of indices of phenotypic plasticity of first-year seedlings of species in different environmental conditions (e.g. light) is still questionable. The shade-intolerant species showed higher values of photosynthetic plasticity indices than shade-tolerant species according to some studies [13, 89]. The results of other studies suggest that mean phenotypic plasticity was similar for shade-tolerant and shade-intolerant species [14, 36, 48, 84]. Variation in light availability was clearly related to variation in sapling growth [90]. Second-year seedlings of Q. robur were more plastic in physiological features [13, 36, 48] in contrast to first-year seedlings [84]. Resources of the acorn can significantly impact the productivity of first-year seedlings [85]. Phenotypic plasticity for morphological traits was not high in Q. robur. Maximum photosynthetic rates increased with increasing light availability in Q. robur. The results suggest that a greater tolerance of strong irradiance is linked to enhanced physiological plasticity of variables related to photosynthesis, while shade tolerance relies on enhanced plasticity of light-harvesting variables (morphology) [36, 48]. This study found that the phenotypic plasticity indices for all variables rendered a similar value for the two species.
Phenotypic plasticity can increase both community-wide trait means or trait variations, i.e. increase effects at the community level both via the biomass-ratio or the diversity hypothesis [91]. Other studies, focused on the diversity hypothesis, also found that trait plasticity in response to species richness typically increased community productivity [92]. Native species showed similar levels of resource-use efficiency and there was no relationship between species plasticity and resource-use efficiency across species [93]. This was confirmed in our study. However, some studies only analyzed community-level effects and thus did not test how individual-level effects were related to these. CWM represents the community structure of each seedling plot and allows a link to their environmental conditions [2, 3, 94]. This was confirmed in our results, where relationships between measures obtained from individual-level trait data were stronger than relationships with measures obtained from community-level trait data. We showed that the community weighted mean seedling traits were less correlated with canopy openness and LAI. Our results are consistent with previously reported studies. The plant traits showed significant correlations with canopy openness; LAR, SSL, and SRA were positively connected to canopy openness; LA and RTD were negatively correlated [26]. The maximum entropy model has been shown to explain significant variation in species abundances based on their functional traits [95-97]. Our results are consistent with studies which propose that survivorship is driven by interspecific differences in resource uptake and tolerance [44].
The differences in the data obtained by different analysis methods, from our point of view, was related to the locality of the conducted study and the range of investigated ecological conditions which occurred in the Arboretum. Uncovering the links between limiting abiotic factors, functional trait diversity and performance is necessary to better predict seedling responses to changes in abiotic conditions in the future.
The morphological and ecological characteristics of seedlings are particularly important keys to understanding adaptation of trees at this ontogenetic phase to environmental changes. In this study, we tried to determine the patterns of response of functional traits of shade-tolerant and shade-intolerant species along natural environmental light gradients, using an entire-level, trait-based approach by direct and indirect methods. The obtained data on the values of functional traits of A. platanoides and Q. robur indicated that relationships between measures obtained from individual-level trait data were stronger than relationships with measures obtained from community-level trait data in the studied environmental conditions. The variation in the phenotypic plasticity of functional parameters of seedlings and local lights was confirmed. Relationships between the internal and external parameters of the functional traits of A. platanoides and Q. robur seedlings were detected. Our results are required for successful resumption of maple and oak in ex situ conservation. We suggest that this type of study be conducted for human-impacted forests and parks.
Acknowledgements
The study was financially supported by the Institute of Dendrology, Polish Academy of Sciences, Kórnik, Poland.
Funding
The study was financially supported by the Institute of Dendrology, Polish Academy of Sciences, Kórnik, Poland.
Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Olena Blinkova, Roma Żytkowiak, and Andrzej M. Jagodziński. The first draft of the manuscript was written by Blinkova Olena and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate. The Q. robur and A. platanoides seedlings used for experiments in this study were grown in Kornik Arboretum, Poland. We obtained sampling permission from the owner (Institute of Dendrology, PAS) before sampling our experiment materials (seedling above- and below-ground biomass).
Consent for publication. Not applicable.
Competing Interests. The authors declare that they have no financial or non-financial conflict of interest.
- Bruelheide H, Dengler J, Purschke O, Lenoir J, Jiménez-Alfaro B, Hennekens SM, Botta-Dukát Z. et al. Global trait–environment relationships of plant communities. Nature Ecology and Evolution 2018;2:1906–17.
- de la Riva EG, Perez-Ramos IM, Tosto A, Navarro-Fernandez CM, Olmo M, Maranon T, Villar R. Disentangling the relative importance of species occurrence, abundance and intraspecific variability in community assembly: a trait-based approach at the whole-plant level in Mediterranean forests. Oikos 2016a;125(3):354–63.
- de la Riva EG, Olmo M, Poorter H, Ubera JL, Villar R. Leaf mass per area (LMA) and its relationship with leaf structure and anatomy in 34 mediterranean woody species along a water availability gradient. PLOS ONE 2016b;11(2):e0148788.
- Grime JP. Vegetation classification by reference to strategies. Nature 1974;250:26–31.
- Kattge J, Bönisch G, Diaz S, Lavorel S, Prentice IC, Leadley P, Tautenhahn S. et al. TRY plant trait database – enhanced coverage and open access. Global Change Biology 2020;26(1):119–88.
- Violle C, Navas M-L, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E. Let the concept of trait be functional. Oikos 2007;116 (5):882–92.
- Yin Q, Wang L, Lei M, Yue M. The relationships between leaf economics and hydraulic traits of woody plants depend on water availability. The Science of Total Environment 2018;621(3):245–52.
- Diaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B. et al. The global spectrum of plant form and function. Nature 2016;529:167–71.
- Fortunel C, Fine PVA, Baraloto C. Leaf, stem and root tissue strategies across 758 Neotropical tree species. Functional Ecology 2012;26(5):1153–61.
- Lavorel S, Garnier E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Functional Ecology 2002;16(5):545–56.
- Laughlin DC, Laughlin DE. Advances in modeling trait-based plant community assembly. Trends in Plant Science 2013;18(10):584–893.
- Roscher C, Schumacher J, Lipowsky A, Gubsch M, Weigelt A, Pompe S, Kolle O. et al. A functional trait-based approach to understand community assembly and diversity–productivity relationships over 7 years in experimental grasslands. Perspectives in Plant Ecology, Evolution and Systematics 2013;15(3):139–49.
- Valladares F, Niinemets Ü. Shade tolerance, a key plant feature of complex nature and consequences. Annual Review of Ecology, Evolution and Systematics 2008;39(1):237–57.
- Valladares F, Bastias CC, Godoy O, Granda E, Escudero A. Species coexistence in a changing world. Frontiers in Plant Science 2015;6:866.
- Reich PB, Tjoelker MG, Walters MB, Vanderklein DW, Buschena C. Close association of RGR, leaf and root morphology, seed mass and shade tolerance in seedlings of nine boreal tree species grown in high and lowlight. Functional Ecology 1998;12(3):327–38.
- Walters MB, Reich PB. Are shade tolerance, survival, and growth linked? Low light and, nitrogen effects on hardwood seedlings. Ecology 1996;77(3):841–53.
- Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J. et al. The worldwide leaf economics spectrum. Nature 2004;428(6985):821–27.
- Freschet GT, Cornelissen JHC, van Logtestijn RSP, Aerts R. Evidence of the 'plant economics spectrum' in a subarctic flora. Journal of Ecology 2010;98(2):362–73.
- Perez-Ramos IM, Roumet CR, Cruz P, Blanchard A, Autran P, Garnier E. Evidence for a ‘plant community economics spectrum’ driven by nutrient and water limitations in a Mediterranean rangeland of southern France. Journal of Ecology 2012;100(6):1315–27.
- Kramer-Walter KR, Bellingham PJ, Millar TR, Smissen RD, Richardson SJ, Laughlin DC. Root traits are multidimensional: specific root length is independent from root tissue density and the plant economic spectrum. Journal of Ecology 2016;104(5):1299–310.
- Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytologist 2012;193(1):30–50.
- Valverde-Barrantes OJ, Freschet GJ, Roumet C, Blackwood CB. A worldview of root traits: the influence of ancestry, growth form, climate and mycorrhizal association on the functional trait variation of fine-root tissues in seed plants. New Phytologist 2017;215(4):1562–73.
- Reich PB. The world-wide 'fast-slow' plant economics spectrum: a traits manifesto. Journal of Ecology 2014;102(2):275–301.
- Baraloto C, Paine CET, Poorter L, Beauchene J, Bonal D, Domenach AM, Herault B. et al. Decoupled leaf and stem economics in rain forest trees. Ecology Letters 2010;13(11):1338–47.
- Wright IJ, Ackerly DD, Bongers F, Harms KE, Ibarra-Manriquez G, Martinez-Ramos M, Mazer SJ. et al. Relationships among ecologically important dimensions of plant trait variation in seven Neotropical forests. Annals of Botany 2007;99(5):1003–15.
- Shen Y, Gilbert GS, Li W, Fang M, Lu H, Yu S. Linking aboveground traits to root traits and local environment: implications of the plant economics spectrum. Frontiers in Plant Science 2019;10:1412.
- Valverde-Barrantes OJ, Blackwood CB. Root traits are multidimensional: specific root length is independent from root tissue density and the plant economic spectrum: commentary on Kramer-Walter et al. Journal of Ecology 2016;104(5):1311–13.
- Hu Y-K, Pan X, Yang X-J, Liu G-F, Liu X-Y, Song Y-B, Zhang M-Y. et al. Is there coordination of leaf and fine root traits at local scales? A test in temperate forest swamps. Ecology and Evolution 2019;9(15):8714–23.
- Fort F, Volaire F, Guilioni L, Barkaoui K, Navas M-L, Roumet C. Root traits are related to plant water-use among rangeland Mediterranean species. Functional ecology 2017;31(9):1700–09.
- Qi Y, Huang J-L, Zhang S-B. Correlated evolution of leaf and root anatomic traits in Dendrobium (Orchidaceae). AoB Plants 2020;12(4):plaa034.
- Khan A, Zarif N, Yang L, Clothier B, Rewald B. Correlation of leaf and root traits of two angiosperm tree species in Northeast China under contrasting light and nitrogen availabilities. Forests 2021;12(5):596.
- Perez-Harguindeguy N, Diaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, Bret-Harte MS. et al. New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany 2013;61(3):167–234.
- He N, Liu C, Piao S, Sack L, Xu L, Luo Y, He J. et al. Ecosystem traits linking functional traits to macroecology. Trends in Ecology & Evolution 2019;34(3):200–10.
- He N, Li Y, Liu C, Xu L, Li M, Zhang J, He J. et al. Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution 2020;35(10):908–18.
- Kitajima K. Relative importance of photosynthetic traits and allocation patterns as correlates of seedling shade tolerance of 13 tropical trees. Oecologia 1994;98:419–28.
- Valladares F, Chico JM, Aranda I, Balaguer L, Dizengremel P, Manrique E, Dreyer E. Greater high light seedling tolerance of Quercus robur over Fagus sylvatica is linked to a greater physiological plasticity. Trees 2002;16(6):395–403.
- Li Y, Jiang Y, Zhao K, Chen Y, Wei W, Shipley B, Chu C. Exploring trait-performance relationships of tree seedlings along experimentally manipulated light and water gradients. Ecology 2022;103(7):e3703.
- Lusk CH. Leaf area and growth of juvenile temperate evergreens in low light: Species of contrasting shade tolerance change rank during ontogeny. Functional Ecology 2004;18(6):820–8.
- Lusk CH, Piper FH. Seedling size influences relationships of shade tolerance with carbohydrate-storage patterns in a temperate rainforest. Functional ecology 2007;21(1):78–86.
- Poorter L, Rose SA. Light-dependent changes in the relationship between seed mass and seedling traits: A meta-analysis for rain forest tree species. Oecologia 2005;142(3):378–87.
- Poorter L. Growth responses of 15 rain-forest tree species to a light gradient: the relative importance of morphological and physiological traits. Functional Ecology 1999;13(3):396–410.
- Veneklaas EJ, Poorter L. Growth and carbon partitioning of tropical tree seedlings in contrasting light environments. In: Inherent variation in plant growth: physiological mechanisms and ecological consequences. Lambers H, Poorter H, van Vuuren MMI, editors. Leiden (the Netherlands): Backhuys; 1998. p. 41-60.
- Niinemets Ü, Valladares F. Tolerance to shade, drought, and waterlogging of temperate Northern hemisphere trees and shrubs. Ecological Monographs 2006;76(4):521–47.
- Canham CD, Finzi AC, Pacala SW, Burbank DH. Causes and consequences of resource heterogeneity in forests: interspecific variation in light transmission by canopy trees. Canadian Journal of Forest Research 1994;24(2):37–349.
- Kobe RK, Pacala SW, Silander J, Canham C. Juvenile tree survivorship as a component of shade tolerance. Ecological applications 1995;5(2):517–32.
- Capers RS, Chazdon RL, Brenes AR, Alvarado BV. Successional dynamics of woody seedling communities in wet tropical secondary forests. Journal of Ecology 2005;93(6):1071–84.
- Poorter L, Arets EJM. Light environment and tree strategies in a Bolivian tropical moist forest: an evaluation of the light partitioning hypothesis. Plant Ecology 2003;166:295–306.
- Valladares F, Sanchez-Gomez D, Zavala MA. Quantitative estimation of phenotypic plasticity: bridging the gap between the evolutionary concept and its ecological applications. Journal of Ecology 94 2006;(6):1103–16.
- Lortie CJ, Aarssen LW. The specialization hypothesis for phenotypic plasticity in plants. International Journal of Plant Sciences 1996;157(4):484–7.
- Han BC, Umana MN, Mi XC, Liu XJ, Chen L, Wang YQ, Liang Y. et al. The role of transcriptomes linked with responses to light environment on seedling mortality in a subtropical forest, China. Journal of Ecology 2017;105(3):592–601.
- Nowak DJ, Rowntree RA. History and range of Norway maple. Journal of Arboriculture 1990;16(11):291–6.
- Jones EW. Quercus L. Journal of Ecology 1959;47(1):169–222.
- Adamczak A. Expansion of Acer platanoides L. in areas freed from human impact. Biodiversity Research and Conservation 2006;3–4:333–6.
- Lapointe M, Brisson J. A comparison of invasive Acer platanoides and native A. saccharum first-year seedlings: growth, biomass distribution and the influence of ecological factors in a forest understory. Forests 2012;3(2):190–206.
- O'Donnell L, Pickles BJ, Campbell CM, Moulton LL, Hauwert NM, Gorzelak MA. Native tree and shrub canopy facilitates oak seedling regeneration in semiarid woodland. Ecosphere 2020;11(2):e03017.
- Garwood NC. Functional morphology of tropical tree seedlings. In: The ecology of tropical forest tree seedlings. Swaine, M.D., editor. Parthenon: New York;1996.
- Didukh YP. Fundamentals of bioindication. Kyiv: Naukova Dumka;2012.
- Ellenberg H. Vegetation Ecology of Central Europe. 4th ed. Cambridge: Cambridge University Press;1998.
- Tichy L, Axmanova I, Dengler J, Guarino R, Jansen F, Midolo G. et al. Ellenberg-type indicator values for European vascular plant species. Journal of Vegetation Science 2023;34:e13168.
- Ellenberg H, Leuschner C. Zeigerwerte der Pflanzen Mitteleuropas (Indicator values of vascular plants in Central Europe). In: Ellenberg H, Leuschner C, editors. Vegetation Mitteleuropas mit den Alpen (Vegetation of Central Europe including the Alps), 6th edition. Stuttgart: Ulmer;2010.
- Holtland WJ, ter Braak CJF, Schouten MGC. Iteratio: calculating environmental indicator values for species and releves. Applied Vegetation Science 2010;13:369–77.
- Machado J-L, Reich PB. Evaluation of several measures of canopy openness as predictors of photosynthetic photon flux density in deeply shaded conifer-dominated forest understory. Canadian Journal of Forest Research 1999;29(9):1438–44.
- Gomez-Aparicio L, Canham CD, Martin PH. Neighbourhood models of the effects of the invasive Acer platanoides on tree seedling dynamics: Linking impacts on communities and ecosystems. Journal of Ecology 2008;96(1):78–90.
- Kong D, Ma C, Zhang Q, Li L, Chen X, Zeng H, Guo D. Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytologist 2014;203:863–72.
- Liese R, Alings K, Meier IC. Root branching is a leading root trait of the plant economics spectrum in temperate trees. Frontiers in Plant Science 2017;8(315):1–12.
- Valladares F, Wright SJ, Lasso E, Kitajima K, Pearcy RW. Plastic phenotypic response to light of 16 congeneric shrubs from a Panamanian rainforest. Ecology 2000;81(7):1925–36.
- Garnier E, Cortez J, Billes G, Navas M-L, Roumet C, Debussche M, Laurent G. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 2004;85(9):2630–7.
- Dyderski MK, Jagodziński AM. Drivers of invasive tree and shrub natural regeneration in temperate forests. Biological Invasions 2018;20:2363–79.
- Xia S-W, Cao M, Yang X, Chen J, Goodale UM. Fine scale heterogeneity of soil properties causes seedling spatial niche separation in a tropical rainforest. Plant and Soil 2019;438(1/2):435–45.
- Uriarte M, Canham CD, Thompson J, Zimmerman JK, Brokaw N. Seedling recruitment in a hurricane-driven tropical forest: light limitation, density-dependence and the spatial distribution of parent trees. Journal of Ecology 2005;93(2):291–304.
- Coble AP, Cavaleri MA. Light drives vertical gradients of leaf morphology in a sugar maple (Acer saccharum) forest. Tree Physiology 2014;34(2):146–58.
- Saldaña-Acosta A, Meave JA, Sanchez-Velasquez LR. Seedling biomass allocation and vital rates of cloud forest tree species: Responses to light in shade house conditions. Forest Ecology and Management 2009;258(7):1650–9.
- Umana MN, Arellano G, Swenson NG, Zambrano J. Tree seedling trait optimization and growth in response to local-scale soil and light variability. Ecology 2021;102(4):e03252.
- Kozlowski TT, Pallardy SG. Acclimation and adaptive responses of woody plants to environmental stresses. The Botanical Review 2002;68(2):270–334.
- Kolb A, Barsch F, Diekmann M. Determinants of local abundance and range size in forest vascular plants. Global Ecology and Biogeography 2006;15(3):237–47.
- Kitajima K, Poorter L. Functional basis for resource niche partitioning by tropical trees. In Tropical forest community ecology. Carson WP, Schnitzer SA, editors. Oxford: Wiley-Blackwell;2008. p. 160-181.
- Weemstra M, Mommer L, Visser EJW, van Ruijven J, Kuyper TW, Mohren GMJ, Sterck FJ. Towards a multidimensional root trait framework: a tree root review. New Phytologist 2016;211:1159–69.
- Lohier T, Jabot F, Meziane D, Shipley B, Reich PB, Deffuant G. Explaining ontogenetic shifts in root-shoot scaling with transient dynamics. Annals of Botany 2014;114(3):513–24.
- Bonifas KD, Walters DT, Cassman K, Lindquist JL. Nitrogen supply affects root:shoot ratio in corn and velvetleaf (Abutilon theophrasti). Weed Science 2009;53:670–5.
- Maškova T, Herben T. Root: shoot ratio in developing seedlings: How seedlings change their allocation in response to seed mass and ambient nutrient supply. Ecology and Evolution 2018;8(14):7143–50.
- Niinemets Ü. Growth of young trees of Acer platanoides and Quercus robur along a gap-understory continuum: Interrelationships between allometry, biomass partitioning, nitrogen, and shade tolerance. International Journal of Plant Sciences 1998;159(2):318–30.
- Bergmann J, Ryo M, Prati D, Hempel S, Rillig MC. Root traits are more than analogues of leaf traits: the case for diaspore mass. New Phytologist 2017;216(4): 1130–9.
- Reinhart KO, Maestre FT, Callaway RM. Facilitation and inhibition of seedlings of an invasive tree (Acer platanoides) by different tree species in a mountain ecosystem. Biological Invasions 2006;8(2):231–40.
- van Hees AFM. Growth and morphology of pedunculate oak (Quercus robur L.) and beech (Fagus sylvatica L.) seedlings in relation to shading and drought. Annals of Forest Science 1997;54(1):9–18.
- Welander, N.T., Ottosson, B. 1998. The influence of shading on growth and morphology in seedlings of Quercus robur L. and Fagus sylvatica L. Forest Ecology and Management 1998;107(1–3):117–26.
- Sevillano I, Short I, Grant J, O’Reilly C. Effects of light availability on morphology, growth and biomass allocation of Fagus sylvatica and Quercus robur seedlings. Forest Ecology and Management 2016;374:11–9.
- Record S, Kobe RK, Vriesendorp CF, Finley AO. Seedling survival responses to conspecific density, soil nutrients, and irradiance vary with age in a tropical forest. Ecology 2016;97(9):2406–15.
- Agathokleous E, Belz RG, Kitao M, Koike T, Calabrese EJ. Does the root to shoot ratio show a hormetic response to stress? An ecological and environmental perspective. Journal of Forestry Research 2019;30(5):1569–80.
- Strauss-Debenedetti S, Bazzaz F. Photosynthetic characteristics of tropical trees along successional gradients. In: Mulkey SS, Chazdon RL, Smith AP, editors. Tropical forest plant ecophysiology. Springer, Boston, MA;1996.
- Finzi AC, Canham CD. Sapling growth in response to light and nitrogen availability in a southern New England forest. Forest Ecology and Management 2000;131(1-3):153–65.
- Luo S, Schmid B, Wagg C, Chen Y, Jiang B, Liang M, Liu X, Yu S. Community-wide trait means and variations affect biomass in a biodiversity experiment with tree seedlings. OIKOS 2020;129(6):799–810.
- Sapijanskas J, Paquette A, Potvin C, Kunert N, Loreau M. Tropical tree diversity enhances light capture through crown plasticity and spatial and temporal niche differences. Ecology 2014;95(9):2479–92.
- Funk JL. Differences in Plasticity between Invasive and Native Plants from a Low Resource Environment. Journal of Ecology 2008;96(6):1162–73.
- Johnson DJ, Condit R, Hubbell SP, Comita LS. Abiotic niche partitioning and negative density dependence drive tree seedling survival in a tropical forest. Proceedings of Royal Society B 2017;284(1869):20172210.
- Merow C, Latimer AM, Silander JA. Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation. Ecology 2011;92(7):1523–37.
- Shipley B, Laughlin DC, Sonnier G, Otfinowski R. A strong test of a maximum entropy model of trait-based community assembly Ecology 2011;92(2):507–17.
- Sonnier G, Shipley B, Navas M-L. Plant traits, species pools and the prediction of relative abundance in plant communities: a maximum entropy approach Journal of Vegetation Science 2010;21(2):318–31.
No competing interests reported.