https://orcid.org/0000-0002-5084-9390
Abstract
Seed mineral nutrition is essential for early seedling establishment, and varies under different environmental conditions. However, the intraspecific variation of multi-elements in seeds and the relative effects of climate and soil on seed elements remain unclear, even though understanding these factors is crucial for predicting plant reproductive responses to global changes. Here, we sampled seeds from Euptelea pleiospermum across 18 populations in China. We quantified the inter-population variation of 12 elements in the seeds and analysed their relationship with soil characteristics and climatic variables. We also explored the relationship of N and P concentrations between seeds and leaves. Results showed that seed elements were highly variable across different populations, with macroelements exhibiting lower variability than most of the microelements. Along the latitudinal gradient, the concentrations of K, Ca, Fe and Al in seeds increased, while the concentrations of C and Mn decreased. The stoichiometry of seed elements did not significantly correlate with latitude. Seed element concentrations were associated with both soil and climatic variables, and the influence of soil conditions on intraspecific variations is comparable to or even greater than climatic factors. However, seed stoichiometry was less related to environmental factors. Seeds had higher P but lower N than leaves, with no correlation between seed elements and leaf elements. Our findings suggest that mountain tree species respond to different local environments by adjusting seed element concentrations while maintaining relatively stable seed stoichiometry. We emphasize that, in addition to climate change, soil conditions should be considered when predicting the influence of environmental changes on the elemental composition of plant reproductive organs.
摘要
种子矿质营养对幼苗的早期建立具有十分重要的作用，其在不同的环境条件下常发生变化。然而，种子元素的种内变异以及气候和土壤因素对这种变异的相对影响仍不明确。本研究对领春木(Euptelea pleiospermum)在中国分布区内的18个种群进行种子采样，测量了12种种子元素(C、N、P、K、Ca、Mg、Fe、Mn、B、Zn、Cu、Al)的浓度，并分析了它们与土壤元素及气候变量的关系。此外，我们还探究了N、P元素在种子与叶片之间的关联性。研究发现：种子元素浓度在不同种群间存在较大变异，其中大量元素的变异程度低于绝大多数微量元素。沿纬度梯度，种子中K、Ca、Fe及Al浓度升高， 而C和Mn浓度降低；种子元素化学计量比与纬度之间无显著相关性。种子元素浓度与土壤及气候变量密切关联，且土壤条件对种子元素种内变异的影响接近或大于气候因素；而种子元素的化学计量特征受环境因子的影响较小。种子的P浓度高于叶片，而其N浓度则低于叶片，且N、P元素在种子与叶片之间未呈现显著的相关性。上述研究结果表明，山地树种可通过调节种子元素的浓度，并保持相对稳定的化学计量比，来适应不同的环境条件。同时，本研究强调在预测环境变化对植物繁殖器官元素组成的影响时，除气候变化外，还应考虑土壤条件。
INTRODUCTION
The concentration and stoichiometry of plant elements are important for plant survival, growth and reproduction (Han et al. 2011; Mengel and Kirkby 2001; Xie et al. 2023; Yu et al. 2023). The seed stage is a crucial part of plant life history, with seed element concentrations closely relating to seed germination and early seedling growth (Cochrane et al. 2015; Lamont and Groom 2002; Soriano et al. 2011). Generally, seeds need to maintain adequate mineral nutrition and stable elemental compositions to facilitate seedling establishment (Fenner 1986; Yan et al. 2016). For plants with a wide distribution range, variations in seed elements often occur among different populations due to their adaptive differentiation to heterogeneous environmental conditions (Cochrane et al. 2015; De Frenne et al. 2011). Understanding intraspecific variation in seed elements is essential for predicting how seed element concentrations respond to environmental changes, providing important implications for plant management and conservation actions. However, most studies have focussed solely on seed macroelements (De Frenne et al. 2011; Sun et al. 2012; Wu et al. 2018), with little attention paid to microelements, despite their essential role in plant performance and fitness (Ågren and Weih 2020). For example, microelements such as copper (Cu), iron (Fe) and zinc (Zn) are key regulators of enzyme activities in different plant tissues (Ågren and Weih 2012). Additionally, in the context of rapid climate change, ecologists may prioritize revealing the influence of climatic factors on seed elements, while the influence of soil elements on seed elements is less explored, except in studies of crop species (Kulczycki et al. 2022; Samarah et al. 2004; Wei et al. 2018). Therefore, it remains largely unclear how climate and soil influence intraspecific variation in seed element concentrations.
As most plant elements are absorbed from soils by roots, seed element concentrations may be closely related to soil conditions (Lamont and Groom 2013; Vaughton and Ramsey 2001). For example, Sun et al. (2012) found that the concentrations of K and Mg in seeds of Quercus variabilis were positively correlated with those in the soils. Conversely, there are also negative correlations between seed elements and soil elements. During the process of reproductive growth, plants accumulate elements in seeds or fruits (Groom and Lamont 2010; Lambers et al. 2008) to ensure seedling establishment under low soil nutrient availability (Fenner 1986). For example, Groom and Lamont (2010) found seeds from Proteaceae plants accumulated higher P when soils were less enriched with P. Thus, the element concentrations of seeds could partially reflect plant adaptability to soil conditions and soil element availability has an important influence on variation in seed elements, especially in species with a widespread distribution. However, investigations of the relationship between multi-elements in seeds and soils are still limited.
Intraspecific variation of seed elements can be influenced by climatic variables as plant element concentrations often vary with temperature and precipitation at broad spatial scales (Han et al. 2011; Reich and Oleksyn 2004). For example, Carón et al. (2014) found that seed element concentrations of two Acer species were influenced by different climatic conditions along a wide latitudinal gradient. Wei et al. (2018) reported that seed N and P concentrations in Sinojackia huangmeiensis increased under extreme precipitation-induced waterlogging. Additionally, experimental works have shown that warming and reduction in rainfall altered plant elemental composition and stoichiometric variations (León-Sánchez et al. 2020; Tian et al. 2019). Yet, the relative importance of the effects of different climatic factors on seed element variations is seldom explored. Moreover, solar radiation may be another potentially important climatic factor influencing seed element concentrations, but it is largely neglected.
Besides these abiotic variables, seed elements may also be related to biotic factors, such as the status of elements in other plant organs. The partitioning of elements among plant organs is interdependent to some extent (Hu et al. 2018; Kerkhoff et al. 2006). For example, Hu et al. (2018) observed a strong positive relationship for N and P concentrations and N:P ratios in whole aboveground plant organs. Moreover, element concentrations and stoichiometric ratios in different plant organs are closely related to biological functions (Hocking 1986; Hu et al. 2018; Sardans and Peñuelas 2015). Minden and Kleyer (2014) found that leaves had higher N concentration and N:P ratio than other plant organs because of increased photosynthetic activity in leaves and the higher demand for N. In contrast, P is preferentially accumulated in the seeds because P is more limiting and in higher demand for seedling establishment (Bu et al. 2019; Groom and Lamont 2010; Hocking 1986; Lamont and Groom 2013). However, there are still few empirical studies about how elemental composition in plant reproductive organs is associated with that in vegetative organs. Thus, in this study, we also explore the relationships between seed and leaf elements for N and P, as these two elements play pivotal roles in plant growth, photosynthesis and metabolic processes (Elser et al. 2007; Tian et al. 2018).
Euptelea pleiospermum (Eupteleaceae) is a rare and relict mountain tree species with a wide distributional range across China (Fu and Jin 1992). It generally grows along the edges of river valleys and is usually dominant in the riparian plant communities (Jiang et al. 2002; Wei et al. 2010). As a widespread species, some studies have found intraspecific variation in the functional traits of E. pleiospermum, such as leaf traits and seed morphological traits (Meng et al. 2017; Wu et al. 2018, 2021). The variations in these functional traits have been shown to be attributed to environmental heterogeneity (Meng et al. 2017; Wu et al. 2018). This tree species also appears to have been impacted by climate change over the last few decades, with documented evidence of northward migration (Wei et al. 2015). Consequently, E. pleiospermum provides an excellent opportunity to study intraspecific variability in seed macro- and microelements for mountain trees.
Here we investigate variations in 12 elements (i.e. macroelements: C, N, P, K, Ca, Mg; and microelements: Fe, Mn, B, Zn, Cu, Al) and the stoichiometry (C:N, C:P, N:P) of E. pleiospermum in 18 natural populations across its geographical distribution in China. We analyse the relationship between environmental factors (soil characteristics, climate variables) and seed element concentrations and stoichiometry. Additionally, we explore the relationships between seed elements and leaf elements. Specifically, we aim to address the following questions: (1) Are there substantial variations in seed element concentrations and stoichiometry among different populations of this mountain tree species? If so, do they vary significantly along the latitudinal gradient? (2) How are seed elements and soil elements correlated? (3) What are the major environmental factors influencing seed element concentrations and stoichiometry? Soil characteristics, climatic variables or both? (4) What are the relationships of N and P concentration between plant reproductive organs (seeds) and vegetative organs (leaves)?
MATERIALS AND METHODS
Studied species and study area
Euptelea pleiospermum (Eupteleaceae) is a deciduous and tertiary-relict tree species occurring primarily in mountain riparian forests. It is listed as a rare and endangered species in the China Plant Red Data Book (Fu and Jin 1992). This species flowers before bud burst in early spring (Endress 1986). The fruits are indehiscent samaras (referred to as seeds in this study); they do not crack under natural conditions, and the appendages and seeds are always together in the wild. Seeds of this species ripen from September to October along the latitudinal gradient. The seeds are very small, with weights ranging from 1.75 to 3.87 mg across the 18 studied populations (Wu et al. 2018; Fig. 1), and they are dispersed by gravity, wind and water. During seed germination, the seedlings emerge directly from the samaras. We sampled seeds of E. pleiospermum from 18 natural populations across the species’ geographical distribution range in China (Fig. 1; Supplementary Table S1) (Fu and Jin 1992; Wei et al. 2015). The sampling locations spanned from subtropical to temperate climate zones, with mean annual temperatures (MAT) ranging from 6.5 to 13.7 °C and mean annual precipitation (MAP) ranging from 634 to 1631 mm (Meng et al. 2017). The dominant soil types, as defined by Chinese Soil Taxonomy (CST), include Argi-Udic Ferrosols (red soil), Hapli-Udic Argosols (brown soil) and Hapli-Ustic Argosols (cinnamonic soil) in subtropical to temperate areas of China.
Location of 18 populations of Euptelea pleiospermum across its geographic distribution in China. See Supplementary Table S1 for site code. The picture inserted in the top right corner of this figure shows the seeds of E. pleiospermum.
Seed and soil sampling
According to the timing of seed maturity, we collected mature and fresh seeds from approximately five mother trees (Supplementary Table S1) in each of the 18 populations of E. pleiospermum across China in autumn of 2016. We collected about 250 g (fresh weight) seeds per tree, sampling a total of 89 mother trees. Selected trees from the same populations were at least 50 m apart. After collection, seeds were placed in cloth bags, tagged and then sent to the laboratory, where they were air-dried for about 1 month. For each population, soil samples were collected within a 1-m diameter around three randomly selected mother trees. After removing plant residues and rock fragments, soil samples were collected from 0 to 30 cm depth. In the laboratory, soil samples were air-dried to constant weight before chemical analyses.
Chemical analyses
Seed samples were oven-dried at 60 °C to constant weight, then ground and passed through a 2-mm sieve. We used an inductively coupled plasma optical emission spectrometer (ICP-OES) (Optima 8000, PerkinElmer, USA) to measure the concentrations of seed K, Ca, Mg, Fe, Mn, B, Zn, Cu and Al. Prior to measurement, approximately 0.2500 g of seed samples were digested in an acid solution (5.0 mL HNO3 and 1.0 mL H2O2) using a microwave digestion system (Ethos One Milestone, Italy) and then diluted to 100 mL with deionized water. The solution was filtered through a 0.45-μm cellulose acetate microporous membrane. The concentration of each element in the solution of seed samples (Csolution) was measured using the ICP-OES, based on the standard solution of each element in the standard reference materials. Finally, we calculated the concentration of each element in seeds (Cseed) using the following formula:
In this formula, Cseed (mg g−1) is the concentration of a specific element in seeds, Csolution (mg L−1) is the concentration of that element in the solution of seed samples, Vsolution (L) is the volume of the solution of seed samples and mseed (g) is the mass of the tested seed samples.
Soil samples were ground and sieved through a 100-mesh sieve. As with the analytical method for seed elements, soil K, Ca, Mg, Fe, Mn, B, Zn, Cu and Al concentrations were also measured by the ICP-OES (Optima 8000, PerkinElmer, USA). Before chemical analysis with the ICP-OES, approximately 0.5000 g soil samples were digested using a microwave digestion system in an acid solution consisting of 5.0 mL HNO3, 2.0 mL HCl and 2.0 mL HF, and then diluted with deionized water to 100 mL. The solution was filtered through a 0.45-μm cellulose acetate microporous membrane, and the concentration of each element was measured by the ICP-OES.
Note that data of seed and soil C, N and P and soil pH were taken from a previous study (Wu et al. 2018).
Climatic variables and leaf data
Climate data with a spatial resolution of 30 s (~1 km2) were downloaded from WorldClim v1.4 (http://www.worldclim.org/). These data represent the average values from 1950 to 2000. We used ArcGIS 10.2 to extract MAT, MAP and the mean annual photosynthetically active radiation (PAR) for each site based on their geographic coordinates. Leaf N and leaf P data were previously collected in the growing season of 2013 from the same populations by Meng et al. (2017).
Statistical analysis
Seed element variation was quantified by the coefficient of variation (CV) for each seed element concentration and stoichiometry metric (Han et al. 2011). The formula for calculating CV is as follows:
In this formula, σ and μ are the standard deviation and mean value of seed element concentration of the 18 populations, respectively.
Before data analysis, MAP, seed, leaf and soil element concentrations and stoichiometry were loge-transformed to improve data normality when necessary. All statistical analyses were performed in R 4.0.3 (R Core Team 2020).
Firstly, we used linear regression analysis by the ‘lm’ function in the ‘stats’ package to explore how the concentration and stoichiometry of seed elements change with latitudinal gradient. Then the Pearson correlation by the ‘cor’ function in the ‘corrgram’ package was used to analyse the relationship between seed elements and soil elements.
Subsequently, multiple regressions using the ‘MASS’ package were performed to examine the relationships between seed elements (i.e. concentration and stoichiometry) and environmental variables (i.e. climatic factors and soil variables). Because the measured soil variables (including pH, macroelements, microelements and stoichiometries) are highly correlated (Supplementary Fig. S1), we used the ‘PCA’ function in the ‘FactoMineR’ package to conduct principal components analysis (PCA) to reduce collinearity in the soil data (De Frenne et al. 2011). The first three principal components (PC1, PC2 and PC3) would be used in the models. Then, the collinearity among all environmental variables (i.e. MAT, MAP, PAR, soil PC1, PC2 and PC3) were tested by Pearson correlation analysis. The correlation coefficient between them was all less than 0.65 (Supplementary Table S3), indicating that the collinearity between them was low. Before being added into a multiple regression model, all environmental variables were standardized by subtracting the mean value of the variable and dividing by one standard deviation. After the initial model fit, including all of the potential predictor variables, we used a stepwise model selection routine with the ‘stepAIC’ function in the ‘MASS’ package to select the best-fit model based on the Akaike information criterion (AIC) (Niinemets 2015).
Lastly, we used Pearson correlation to analyse the relationship between seed and leaf element concentrations. In addition, we used t-test to compare the means of N, P concentrations and N:P ratio in seeds and leaves.
RESULTS
PCA of soil variables
The first three PCA axes together explained 73.90% of the variability in soil variables (Supplementary Table S2). The first PCA axis (PC1) was positively correlated with soil pH, Ca, Mg, Fe, Zn, Cu and Al (r ≥ 0.62) and the second PCA axis (PC2) was positively correlated with soil C, N, P and Mn (r ≥ 0.50). The third PCA axis (PC3) was positively correlated with soil C, P and Mg (r ≥ 0.50) (Supplementary Table S2).
Seed element concentration and stoichiometric variability
Seed elements of E. pleiospermum exhibited different degrees of inter-population variation, as indicated by varying standard deviations (SD) for the seed element concentrations and more than a 33-fold difference in their CV (Table 1). Specifically, the CV for individual elemental concentrations showed a wide range, with a minimum of 2.40% and a maximum of 80.80%. For seed macroelements, seed C concentration was nearly constant with a CV of 2.40%. Seed N concentration showed relative stability with a CV of 12.38%. Seed P, K, Ca and Mg concentrations had the approximate CVs of 20%. For seed microelements, Fe, B, Zn and Cu concentrations varied substantially (CVs > 35%) across the 18 sites. For seed stoichiometry, the CV of the C:P ratio, N:P ratio and C:N ratio was 25.64%, 23.99% and 12.20%, respectively.
Seed element concentrations (mg g−1) and stoichiometry of Euptelea pleiospermum for 18 natural populations in China
| Seed element/stoichiometry | Mean ± SD | Minimum | Maximum | CV (%) |
|---|---|---|---|---|
| C | 486.59 ± 11.69 | 471.36 | 509.50 | 2.40 |
| N | 12.92 ± 1.60 | 10.13 | 15.65 | 12.38 |
| P | 1.71 ± 0.38 | 0.96 | 2.34 | 22.55 |
| K | 17.09 ± 3.52 | 10.52 | 26.97 | 20.59 |
| Ca | 8.88 ± 1.39 | 5.84 | 11.05 | 15.61 |
| Mg | 3.20 ± 0.69 | 2.04 | 4.80 | 21.52 |
| Fe | 0.1827 ± 0.0700 | 0.0804 | 0.3071 | 38.33 |
| Mn | 0.0571 ± 0.0152 | 0.0362 | 0.0938 | 26.64 |
| B | 0.1391 ± 0.0758 | 0.0703 | 0.3169 | 54.52 |
| Zn | 0.0372 ± 0.0301 | 0.0023 | 0.0954 | 80.80 |
| Cu | 0.0920 ± 0.0326 | 0.0716 | 0.2047 | 35.46 |
| Al | 0.2071 ± 0.0242 | 0.1715 | 0.2446 | 11.66 |
| C:N | 38.72 ± 4.72 | 32.16 | 47.74 | 12.20 |
| C:P | 309.72 ± 79.42 | 206.35 | 510.08 | 25.64 |
| N:P | 8.10 ± 1.94 | 5.88 | 13.18 | 23.99 |
| Seed element/stoichiometry | Mean ± SD | Minimum | Maximum | CV (%) |
|---|---|---|---|---|
| C | 486.59 ± 11.69 | 471.36 | 509.50 | 2.40 |
| N | 12.92 ± 1.60 | 10.13 | 15.65 | 12.38 |
| P | 1.71 ± 0.38 | 0.96 | 2.34 | 22.55 |
| K | 17.09 ± 3.52 | 10.52 | 26.97 | 20.59 |
| Ca | 8.88 ± 1.39 | 5.84 | 11.05 | 15.61 |
| Mg | 3.20 ± 0.69 | 2.04 | 4.80 | 21.52 |
| Fe | 0.1827 ± 0.0700 | 0.0804 | 0.3071 | 38.33 |
| Mn | 0.0571 ± 0.0152 | 0.0362 | 0.0938 | 26.64 |
| B | 0.1391 ± 0.0758 | 0.0703 | 0.3169 | 54.52 |
| Zn | 0.0372 ± 0.0301 | 0.0023 | 0.0954 | 80.80 |
| Cu | 0.0920 ± 0.0326 | 0.0716 | 0.2047 | 35.46 |
| Al | 0.2071 ± 0.0242 | 0.1715 | 0.2446 | 11.66 |
| C:N | 38.72 ± 4.72 | 32.16 | 47.74 | 12.20 |
| C:P | 309.72 ± 79.42 | 206.35 | 510.08 | 25.64 |
| N:P | 8.10 ± 1.94 | 5.88 | 13.18 | 23.99 |
The seed C, N and P were taken from a previous study (Wu et al. 2018). The elements in bold are microelements. Abbreviations: CV = coefficient of variation, SD = standard deviation.
Seed element concentrations (mg g−1) and stoichiometry of Euptelea pleiospermum for 18 natural populations in China
| Seed element/stoichiometry | Mean ± SD | Minimum | Maximum | CV (%) |
|---|---|---|---|---|
| C | 486.59 ± 11.69 | 471.36 | 509.50 | 2.40 |
| N | 12.92 ± 1.60 | 10.13 | 15.65 | 12.38 |
| P | 1.71 ± 0.38 | 0.96 | 2.34 | 22.55 |
| K | 17.09 ± 3.52 | 10.52 | 26.97 | 20.59 |
| Ca | 8.88 ± 1.39 | 5.84 | 11.05 | 15.61 |
| Mg | 3.20 ± 0.69 | 2.04 | 4.80 | 21.52 |
| Fe | 0.1827 ± 0.0700 | 0.0804 | 0.3071 | 38.33 |
| Mn | 0.0571 ± 0.0152 | 0.0362 | 0.0938 | 26.64 |
| B | 0.1391 ± 0.0758 | 0.0703 | 0.3169 | 54.52 |
| Zn | 0.0372 ± 0.0301 | 0.0023 | 0.0954 | 80.80 |
| Cu | 0.0920 ± 0.0326 | 0.0716 | 0.2047 | 35.46 |
| Al | 0.2071 ± 0.0242 | 0.1715 | 0.2446 | 11.66 |
| C:N | 38.72 ± 4.72 | 32.16 | 47.74 | 12.20 |
| C:P | 309.72 ± 79.42 | 206.35 | 510.08 | 25.64 |
| N:P | 8.10 ± 1.94 | 5.88 | 13.18 | 23.99 |
| Seed element/stoichiometry | Mean ± SD | Minimum | Maximum | CV (%) |
|---|---|---|---|---|
| C | 486.59 ± 11.69 | 471.36 | 509.50 | 2.40 |
| N | 12.92 ± 1.60 | 10.13 | 15.65 | 12.38 |
| P | 1.71 ± 0.38 | 0.96 | 2.34 | 22.55 |
| K | 17.09 ± 3.52 | 10.52 | 26.97 | 20.59 |
| Ca | 8.88 ± 1.39 | 5.84 | 11.05 | 15.61 |
| Mg | 3.20 ± 0.69 | 2.04 | 4.80 | 21.52 |
| Fe | 0.1827 ± 0.0700 | 0.0804 | 0.3071 | 38.33 |
| Mn | 0.0571 ± 0.0152 | 0.0362 | 0.0938 | 26.64 |
| B | 0.1391 ± 0.0758 | 0.0703 | 0.3169 | 54.52 |
| Zn | 0.0372 ± 0.0301 | 0.0023 | 0.0954 | 80.80 |
| Cu | 0.0920 ± 0.0326 | 0.0716 | 0.2047 | 35.46 |
| Al | 0.2071 ± 0.0242 | 0.1715 | 0.2446 | 11.66 |
| C:N | 38.72 ± 4.72 | 32.16 | 47.74 | 12.20 |
| C:P | 309.72 ± 79.42 | 206.35 | 510.08 | 25.64 |
| N:P | 8.10 ± 1.94 | 5.88 | 13.18 | 23.99 |
The seed C, N and P were taken from a previous study (Wu et al. 2018). The elements in bold are microelements. Abbreviations: CV = coefficient of variation, SD = standard deviation.
Latitudinal patterns of seed element concentration and stoichiometry
The concentration of half of the seed elements displayed significant latitudinal trends (Fig. 2). For macroelements, seed C decreased significantly with the increase of latitude (Fig. 2a), while seed K and seed Ca increased (Fig. 2d and e). For microelements, seed Fe and Al increased along the latitudinal gradient (Fig. 2g and l), while seed Mn decreased (Fig. 2h). We observed no significant relationships between latitude and any of the seed stoichiometry metrics (Fig. 3).
Latitudinal patterns of seed macroelement and microelement concentrations for 18 natural populations of Euptelea pleiospermum in China. Solid lines indicate relationships with P < 0.05. The latitudinal patterns of seed C, N and P were taken from Wu et al. (2018).
Latitudinal patterns of seed stoichiometry for 18 natural populations of Euptelea pleiospermum in China.
Correlations between seed elements and soil elements
We observed significant relationships between seed and soil element concentrations for most elements, including C, K, Ca, Mg, Fe, Mn and Al (Fig. 4). One seed element concentration was significantly correlated with several soil element concentrations and stoichiometry (Fig. 4). For example, seed K was negatively correlated with soil C and P but was positively correlated with soil C:P and N:P ratios (Fig. 4). Seed Ca was positively correlated with soil pH, Ca, B, Al, but negatively correlated with soil N and P (Fig. 4). In addition, seed Mg and Al were negatively related with soil P and were positively correlated to soil B, soil C:P and N:P ratios (Fig. 4). However, for the same individual element in seed and soil, no significant correlation was found between them, except that seed Ca was positively correlated with soil Ca (Fig. 4). In addition, seed stoichiometry has no significant correlation with any soil element concentrations or soil stoichiometry metrics (Fig. 4).
Pearson correlation between seed variables and soil variables across 18 natural Euptelea pleiospermum populations in China. All variables were loge-transformed. Blue squares indicate positive correlations and red squares indicate negative correlations. The asterisks (*) in the squares indicate significance. ***P < 0.001, **P < 0.01, *P < 0.05.
Across soil elements, soil P was significantly and negatively correlated with five seed element concentrations including seed C, K, Ca, Mg, Fe and Al, but positively correlated with seed C (Fig. 4). Additionally, soil B was positively correlated with seed Ca, Mg, Fe and Al, and negatively correlated with seed C and Mn (Fig. 4). Furthermore, both soil C:P ratio and N:P ratio were positively related to seed K, Mg, Fe and Al, but negatively related to seed C (Fig. 4).
Relationship between environmental variables and seed element concentrations and stoichiometry
Results of the multiple regression models showed that environmental variables (i.e. climatic factors, soil variables) explained a large proportion of seed nutrient variations (Table 2). For seed macroelements, more than 75% of the variation in seed Ca was explained by the predictor variables. In addition, 50%, 55% and 57% of the variation in seed C, K and Mg were explained, respectively. For seed microelements, more than 65% of the variation in seed Fe and Al was explained, and approximately 40% of the variation in seed Mn, Zn and Cu was explained. However, there was no observed relationship between any of the predictor variables and seed N and C:N ratio. Only a small proportion (about 20%) of the variations in seed C:P and N:P ratios was explained.
Regression results showing the relationship between environmental variables and seed element concentration (mg g−1) and stoichiometry among 18 natural populations of Euptelea pleiospermum in China
| Regression equation | AIC | R2 |
|---|---|---|
| C = 6.19*** + 0.006PC2 + 0.016PC3** | −141.79 | 0.50 |
| Na | - | - |
| P = 0.51*** − 0.136MAP + 0.112PC2 | −50.86 | 0.21 |
| K = 2.82*** + 0.106MAT* + 0.059PAR − 0.055PC1 − 0.123PC2* − 0.090PC3 | −61.40 | 0.55 |
| Ca = 2.17*** + 0.061PC1* − 0.115PC2*** − 0.069PC3** | −82.74 | 0.76 |
| Mg = 1.14*** + 0.119MAT* − 0.094MAP − 0.082PC2 | −63.97 | 0.57 |
| Fe = −1.78*** − 0.095MAP − 0.182PAR* + 0.091PC1 − 0.198PC3* | −41.86 | 0.65 |
| Mn = −2.90*** + 0.112MAT* − 0.117PC1* | −52.98 | 0.39 |
| B = −2.09*** − 0.254PAR* | −29.61 | 0.28 |
| Zn = −3.66*** + 0.453MAT + 0.367PAR − 0.483PC2* − 0.488PC3 | −1.62 | 0.41 |
| Cu = −2.43** − 0.160PAR** + 0.108PC1 + 0.082PC2 | −51.62 | 0.48 |
| Al = −1.58*** + 0.045MAT* − 0.051MAP* − 0.076PC3** | −89.41 | 0.66 |
| C:Na | - | - |
| C:P = 5.71*** + 0.114PC3* | −52.90 | 0.23 |
| N:P = 2.07*** − 0.074PAR + 0.099PC3 | −52.45 | 0.21 |
| Regression equation | AIC | R2 |
|---|---|---|
| C = 6.19*** + 0.006PC2 + 0.016PC3** | −141.79 | 0.50 |
| Na | - | - |
| P = 0.51*** − 0.136MAP + 0.112PC2 | −50.86 | 0.21 |
| K = 2.82*** + 0.106MAT* + 0.059PAR − 0.055PC1 − 0.123PC2* − 0.090PC3 | −61.40 | 0.55 |
| Ca = 2.17*** + 0.061PC1* − 0.115PC2*** − 0.069PC3** | −82.74 | 0.76 |
| Mg = 1.14*** + 0.119MAT* − 0.094MAP − 0.082PC2 | −63.97 | 0.57 |
| Fe = −1.78*** − 0.095MAP − 0.182PAR* + 0.091PC1 − 0.198PC3* | −41.86 | 0.65 |
| Mn = −2.90*** + 0.112MAT* − 0.117PC1* | −52.98 | 0.39 |
| B = −2.09*** − 0.254PAR* | −29.61 | 0.28 |
| Zn = −3.66*** + 0.453MAT + 0.367PAR − 0.483PC2* − 0.488PC3 | −1.62 | 0.41 |
| Cu = −2.43** − 0.160PAR** + 0.108PC1 + 0.082PC2 | −51.62 | 0.48 |
| Al = −1.58*** + 0.045MAT* − 0.051MAP* − 0.076PC3** | −89.41 | 0.66 |
| C:Na | - | - |
| C:P = 5.71*** + 0.114PC3* | −52.90 | 0.23 |
| N:P = 2.07*** − 0.074PAR + 0.099PC3 | −52.45 | 0.21 |
A multiple stepwise regression model was used to select the best-fit set of environmental predictors. PC1, PC2, and PC3 were the first three PCA axes of principal components analysis for soil variables, respectively. The significant environmental variables were shown in bold (P < 0.05). Abbreviations: AIC = Akaike information criterion, MAP = mean annual precipitation, MAT = mean annual temperature, PAR = photosynthetically active radiation.
aAn intercept-only model was selected, that is, there were not any other explanatory variables in the final model.
***P < 0.001, **P < 0.01, *P < 0.05.
Regression results showing the relationship between environmental variables and seed element concentration (mg g−1) and stoichiometry among 18 natural populations of Euptelea pleiospermum in China
| Regression equation | AIC | R2 |
|---|---|---|
| C = 6.19*** + 0.006PC2 + 0.016PC3** | −141.79 | 0.50 |
| Na | - | - |
| P = 0.51*** − 0.136MAP + 0.112PC2 | −50.86 | 0.21 |
| K = 2.82*** + 0.106MAT* + 0.059PAR − 0.055PC1 − 0.123PC2* − 0.090PC3 | −61.40 | 0.55 |
| Ca = 2.17*** + 0.061PC1* − 0.115PC2*** − 0.069PC3** | −82.74 | 0.76 |
| Mg = 1.14*** + 0.119MAT* − 0.094MAP − 0.082PC2 | −63.97 | 0.57 |
| Fe = −1.78*** − 0.095MAP − 0.182PAR* + 0.091PC1 − 0.198PC3* | −41.86 | 0.65 |
| Mn = −2.90*** + 0.112MAT* − 0.117PC1* | −52.98 | 0.39 |
| B = −2.09*** − 0.254PAR* | −29.61 | 0.28 |
| Zn = −3.66*** + 0.453MAT + 0.367PAR − 0.483PC2* − 0.488PC3 | −1.62 | 0.41 |
| Cu = −2.43** − 0.160PAR** + 0.108PC1 + 0.082PC2 | −51.62 | 0.48 |
| Al = −1.58*** + 0.045MAT* − 0.051MAP* − 0.076PC3** | −89.41 | 0.66 |
| C:Na | - | - |
| C:P = 5.71*** + 0.114PC3* | −52.90 | 0.23 |
| N:P = 2.07*** − 0.074PAR + 0.099PC3 | −52.45 | 0.21 |
| Regression equation | AIC | R2 |
|---|---|---|
| C = 6.19*** + 0.006PC2 + 0.016PC3** | −141.79 | 0.50 |
| Na | - | - |
| P = 0.51*** − 0.136MAP + 0.112PC2 | −50.86 | 0.21 |
| K = 2.82*** + 0.106MAT* + 0.059PAR − 0.055PC1 − 0.123PC2* − 0.090PC3 | −61.40 | 0.55 |
| Ca = 2.17*** + 0.061PC1* − 0.115PC2*** − 0.069PC3** | −82.74 | 0.76 |
| Mg = 1.14*** + 0.119MAT* − 0.094MAP − 0.082PC2 | −63.97 | 0.57 |
| Fe = −1.78*** − 0.095MAP − 0.182PAR* + 0.091PC1 − 0.198PC3* | −41.86 | 0.65 |
| Mn = −2.90*** + 0.112MAT* − 0.117PC1* | −52.98 | 0.39 |
| B = −2.09*** − 0.254PAR* | −29.61 | 0.28 |
| Zn = −3.66*** + 0.453MAT + 0.367PAR − 0.483PC2* − 0.488PC3 | −1.62 | 0.41 |
| Cu = −2.43** − 0.160PAR** + 0.108PC1 + 0.082PC2 | −51.62 | 0.48 |
| Al = −1.58*** + 0.045MAT* − 0.051MAP* − 0.076PC3** | −89.41 | 0.66 |
| C:Na | - | - |
| C:P = 5.71*** + 0.114PC3* | −52.90 | 0.23 |
| N:P = 2.07*** − 0.074PAR + 0.099PC3 | −52.45 | 0.21 |
A multiple stepwise regression model was used to select the best-fit set of environmental predictors. PC1, PC2, and PC3 were the first three PCA axes of principal components analysis for soil variables, respectively. The significant environmental variables were shown in bold (P < 0.05). Abbreviations: AIC = Akaike information criterion, MAP = mean annual precipitation, MAT = mean annual temperature, PAR = photosynthetically active radiation.
aAn intercept-only model was selected, that is, there were not any other explanatory variables in the final model.
***P < 0.001, **P < 0.01, *P < 0.05.
Among the environmental factors, soil variables seemed to have comparable or slightly stronger relations to seed element variations than climate variables. Eight seed element concentrations and stoichiometry metrics (C, K, Ca, Fe, Mn, Zn, Al and C:P) were significantly correlated with the first three soil PCA axes (PC1, PC2 and/or PC3), and seven metrics (K, Mg, Fe, Mn, B, Cu and Al) were significantly related to MAT and/or PAR (Table 2). However, the associations between MAP and seed element variations were weak, as only seed Al was significantly correlated with MAP.
Relationship between seed elements and leaf elements
The results of the correlation analysis showed that there was no statistically significant relationship between N in seeds and leaves, and between P in seeds and leaves (Fig. 5a and b). Seed N:P was also not correlated with leaf N:P (Fig. 5c). By comparing the mean N and P concentrations in seeds and leaves, we found that leaves had higher N than seeds and seeds have higher P than leaves (Fig. 5d and e). In addition, leaves had higher N:P ratio than seeds (Fig. 5f).
Relationship and comparison of seed and leaf elements (N, P and N:P) for the 18 natural populations of Euptelea pleiospermum in China. ***P < 0.001, **P < 0.01, *P < 0.05.
DISCUSSION
Variability and latitudinal patterns of seed elements
Seed element concentrations and stoichiometry of E. pleiospermum exhibited different degrees of intraspecific variation among the 18 natural populations. All seed macroelements exhibited lower variability than the seed microelements, except for the seed Al element (Table 1), a finding that is in agreement with the stability of limiting elements hypothesis (Han et al. 2011). This hypothesis predicts that elements that are required in higher concentrations in plant tissues and are considered to be more limiting to plant growth show lower variability than elements that are not (Han et al. 2011). Accordingly, variation of seed macroelements (especially N, P and K) is more constrained (i.e. more stable with lower CVs) than that of most seed microelements, which is advantageous for seedling establishment under a range of nutrient conditions in nature. However, seed Al also showed a low variability, with a CV of 11.66%. The possible reason was that Al may be toxic to this species and plant tissues tend to keep the toxic minerals within a certain threshold.
Half of the studied seed element concentrations displayed significant latitudinal patterns. For macroelements, seed K and Ca concentrations increased at northern sites (Fig. 2), which was consistent with findings from other studies. For example, Sun et al. (2012) found that K and Mg concentrations of Q. variabilis acorns increased with increasing latitude in China. These patterns may arise from the biological functions and physiological requirements of the specific element in plant tissues, or the availability of nutrients in the soil (Carón et al. 2014; Han et al. 2011). Because K is highly necessary for photosynthetic tissues and is mainly used as an activator of many enzymes (Larcher 2003), increasing the concentration of this element in seeds at higher latitudes may help seedlings cope with unfavourable environments (e.g. lower temperature and precipitation, as well as a shorter growing season) in northern sites. The seed Ca concentration increased with the increase of latitude, which may be due to the high availability of Ca in northern soil (Sun et al. 2012), as there was a significant positive correlation between seed Ca and soil Ca (Fig. 4). For microelements, we also found an increasing trend with latitude for seed Fe and Al concentrations (Fig. 2). In contrast, seed Mn concentration decreased with the increase of latitude. This can be explained by the fact that Mn is primarily used by plants in photosystem II (Marschner 2012), and the lower photosynthetic capacity in colder northern areas does not require excessive Mn. Consequently, low seed Mn concentration can help seedlings avoid Mn toxicity.
Correlation between environmental factors and seed element variations
In this study, we found that the differences in most seed element concentrations did not mirror those of the same elements in the soil, as there were no significant correlations between them (Fig. 4). However, for vegetative organs, it is demonstrated that a specific element in plant tissues is often directly affected by its concentration in the soil (Han et al. 2011; Hao et al. 2015). For example, Han et al. (2011) found that there were positive correlations between the concentrations of the same elements in leaves and soils. The inconsistent pattern observed between reproductive organs and vegetative organs may be due to the distinct biological function of seeds, which are storage places where elements are accumulated (Lott et al. 1995; Sun et al. 2012). Therefore, seeds need to absorb and accumulate certain concentration of mineral elements to meet the nutrient requirements of early seedling growth, and the concentration of a specific element in the seeds does not necessarily reflect its concentration in the soil.
On the other hand, one specific seed element was significantly associated with multiple other elements in the soil, and a single soil element can also influence multiple seed elements simultaneously (Fig. 4). That is to say, different soil elements shaped the concentration of elements in the seeds, which implies that the overall soil environment may influence the accumulation of elements in seeds. In addition, previous studies showed that variations in elemental concentration within seeds could enable the seeds to adapt to the maternal surrounding environment (De Frenne et al. 2011; Sun et al. 2012). Therefore, intraspecific variations in seed elements might be the basis for the life history strategies of seeds (such as germination, dormancy, seedling establishment, etc.) to adapt to different environments.
Environmental variables explained a large proportion of the variation observed across sites for most of the seed elements in this study (Table 2), suggesting that intraspecific variation in seed element concentrations results from the integrated effects of soil elements, temperature, precipitation, and solar radiation (Carón et al. 2014; Sun et al. 2012). Notably, we found that seed element concentrations were correlated with soil conditions to a similar or even slightly stronger extent than with climatic factors (Table 2). Since plant elements are primarily obtained from the soil, elemental variation in plants could be largely affected by differences in soil elements (Ågren and Weih 2012; Hao et al. 2015). In addition, our results showed that climatic factors also played an important role in influencing the intraspecific variation of seed element concentrations. However, variations in seed stoichiometry were not well explained by these environmental variables (Table 2), and no significant correlations were observed between seed stoichiometry and soil variables (Fig. 4). This may be due to stoichiometric homeostasis, where plants need to maintain a relatively stable element ratio for optimal growth (Markert 1989; Sterner and Elser 2002; Tian et al. 2019). Similarly, Zhou et al. (2015) reported that local soil geochemistry altered element concentrations in Q. variabilis, but did not change elemental ratios.
Particularly, we found that MAT drives variations in seed elements more than MAP, as MAT is significantly related to more seed elements than MAP (Table 2). This may be because temperature is a crucial factor in controlling seed maturity and element accumulation (De Frenne et al. 2011). Therefore, temperature is more closely related to seed elements than precipitation. Similarly, Sun et al. (2013) found that Mg in acorns of oriental oak had a stronger association with MAT than with MAP. The other possible explanation is that E. pleiospermum is found primarily in mountain riparian forests where water is not limited. In a previous study, Meng et al. (2017) concluded that temperature was more strongly related to leaf trait variation of this species than precipitation. In addition, we found that PAR also contributed to the explanation of variations in seed element concentrations (Table 2). This is because the levels of PAR determine the levels of photosynthate, which in turn influence seed mass and the amount of seed nutrient reserves (Murray et al. 2004).
Relationship of elements in seed and leaf
Our results showed that seeds had higher P than leaves and leaves had higher N and N:P ratio than seeds (Fig. 5). This difference in element distribution patterns between seeds and leaves demonstrated that various plant metabolic processes and physiological functions have distinct nutrient requirements (Sardans and Peñuelas 2015). Higher N and N:P ratio were needed for increased photosynthesis in leaves to promote carbon assimilation in plants (Hu et al. 2018; Minden and Kleyer 2014). By contrast, seeds had higher P concentration because seeds contain a lot of nutrients composed of P, such as phytic acid (Bu et al. 2019; Lamont and Groom 2013; Lott et al. 2000). For example, Groom and Lamont (2010) observed that seed P can contribute up to 48% of the total aboveground P for Proteaceae plants. In addition, our findings indicate that P is needed more than N for seedling establishment. Similarly, other studies have shown that P is transported to seeds more efficiently than N, and it is more critical for seedling establishment in many species (Groom and Lamont 2010; Hocking 1986; Tyler and Zohlen 1998).
Pearson correlation analysis showed that there was no significant relationship between seed elements and leaf elements for N and P concentration and N:P ratio (Fig. 5). This may be because seeds need to accumulate adequate nutrients (e.g. phytic acid and proteins) for seedling establishment, regardless of the nutrient status of the leaves. This theory is partially supported by results reported by Yan et al. (2016) who demonstrated that reproductive tissues in Arabidopsis thaliana showed more constrained elemental composition in response to nutrient availabilities than non-reproductive parts. Similarly, Hocking (1986) found that high concentrations of certain elements were in seeds despite the low levels of the same elements in the leaves of the Grevillea spp. However, some caution is required when comparing this finding with other species, floras or ecosystems. To further clarify this relationship, more collection and analysis of elemental data in vegetative and reproductive organs should be conducted in the future.
CONCLUSIONS
Our study showed that there were differences in intraspecific variations and latitudinal patterns of seed macro- and microelement concentrations and stoichiometries of E. pleiospermum across China, which indicated that tree species respond to local environments via regulation of seed element concentrations and maintaining of relatively stable seed element stoichiometry. In addition, we found that seed element concentrations were associated with both soil and climatic variables, with soil conditions playing a comparable role to climatic factors in driving intraspecific variations. Thus, we emphasize that when predicting the impact of environmental changes on plant elemental composition, more attention should be paid to changes in soil conditions (e.g. the effects of soil acidification or mineralization, and nitrogen deposition), rather than focussing solely on climate change.
Furthermore, our study showed that there was no relationship in element concentrations (N and P) between vegetative organs and reproductive organs. This implies that seeds need to accumulate adequate nutrients for reproductive success, regardless of the nutrient status in leaves. However, it should be noted that the comparisons of seed and leaf elements were carried out at the population level rather than at the individual level. Additionally, there is a limitation in our data, that is, the seeds and leaves were not collected in the same year. Therefore, further study is needed to strengthen this conclusion through more rigorous experimental design, and to test the generality of this finding across species and ecosystems.
Supplementary Material
Supplementary material is available at Journal of Plant Ecology online.
Table S1: Site characteristics for the 18 natural populations of Euptelea pleiospermum across its geographical distribution in China.
Table S2: Principal component analysis (PC1, PC2 and PC3) for soil variables estimated for the 18 natural populations of Euptelea pleiospermum in China.
Table S3: Correlations (Pearson coefficient) among all environmental variables.
Figure S1: Pearson correlation between soil variables across 18 natural Euptelea pleiospermum populations in China.
Authors’ Contributions
Hao Wu: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing. Hongjie Meng: Conceptualization, Methodology, Investigation, Data curation. Mingxi Jiang: Conceptualization, Methodology, Writing – review & editing. Xinzeng Wei: Conceptualization, Methodology, Investigation, Supervision, Writing – review & editing.
Funding
National Natural Science Foundation of China (32371653, 32001225, and 31770572); State Key Laboratory of Vegetation and Environmental Change (LVEC-2021kf01).
Acknowledgements
We thank Dr Joseph Elliot at the University of Kansas for his assistance with English language and grammatical editing of the manuscript. We also greatly appreciate the three anonymous reviewers for their helpful comments on the earlier version of this manuscript.
Conflict of interest statement. The authors declare that they have no conflict of interest.
