Optimal leaf-to-root ratio and leaf nitrogen content determined by light and nitrogen availabilities.

Plants exhibit higher leaf-to-root ratios (L/R) and lower leaf nitrogen content (N(area)) in low-light than in high-light environments, but an ecological significance of this trait has not been explained from a whole-plant perspective. This study aimed to theoretically and experimentally demonstrate whether these observed L/R and N(area) are explained as optimal biomass allocation that maximize whole-plant relative growth rate (RGR). We developed a model which predicts optimal L/R and N(area) in response to nitrogen and light availability. In the model, net assimilation rate (NAR) was determined by light-photosynthesis curve, light availability measured during experiments, and leaf temperature affecting the photosynthesis and leaf dark respiration rate in high and low-light environments. Two pioneer trees, Morus bombycis and Acer buergerianum, were grown in various light and nitrogen availabilities in an experimental garden and used for parameterizing and testing the model predictions. They were grouped into four treatment groups (relative photosynthetic photon flux density, RPPFD 100% or 10%×nitrogen-rich or nitrogen-poor conditions) and grown in an experimental garden for 60 to 100 days. The model predicted that optimal L/R is higher and N(area) is lower in low-light than high-light environments when compared in the same soil nitrogen availability. Observed L/R and N(area) of the two pioneer trees were close to the predicted optimums. From the model predictions and pot experiments, we conclude that the pioneer trees, M. bombycis and A. buergerianum, regulated L/R and N(area) to maximize RGR in response to nitrogen and light availability.

Introduction

Plants have the ability to alter their phenotype to maximize fitness according to the external environment. For example, they often change leaf properties and biomass allocation pattern in accordance with light and nutrients conditions [1], [2]. Criteria for determining leaf to root ratio (L/R) have been investigated by many researchers because it could be a major factor dictating plant growth rate and fitness [3]. Thus, elucidating the L/R will be helpful for understanding plant growth strategies in natural ecosystems.

Until now, many researchers have worked with the subject, and proposed the balanced growth hypothesis where plants allocate more biomass to the organ capturing the most limiting resources, such as light and nutrients [4]–[6]. According to this hypothesis, for example, producing more leaves at the sacrifice of root growth is favoured in low-light environments to capture more light to enhance growth rate. However, this hypothesis is only an intuitive explanation and can't propose a quantitative estimation of L/R. Since leaf and root functions are closely interrelated, producing excessive leaves may decrease growth rate due to decreased root functions, such as <span class="Chemical">nitrogen uptake capacity. This lead to an idea that there will be an equilibrium between leaves and roots for optimal biomass allocation that maximizes whole-plant growth rate [1], [7].

Theoretical analyses and experimental confirmation of the hypothesis have been performed for plants in high-light environments. Such studies revealed that the L/R and leaf <span class="Chemical">nitrogen content were mainly optimized to maximize RGR with soil <span class="Chemical">nitrogen availability [7], [8]. In contrast, plants growing in low-light environments generally have higher L/R than those growing in high-light environment regardless of functional groups [1], [9]–[13]. In these studies, however, the high L/R were only explained from the balanced growth hypothesis as mentioned above, and theoretical studies accounting for this biomass allocation pattern are still lacking. Thus, it has not been quantitatively determined whether the high L/R in low-light environments is as a result of maximization of relative growth rate (RGR) to maximize.

We noticed that plants growing in low-light environments have lower leaf <span class="Chemical">nitrogen content per leaf area (N area) and associated lower maximum photosynthetic and dark respn>iration rate [1], [9], [14]. <span class="Chemical">Nitrogen is almost thoroughly absorbed by root and considered to be a primary mineral which dictate amount of photosynthate and growth [15] through the balance between photosynthetic and respiration rate and light availability. For example, higher N area realize higher maximum photosynthetic rate, but if light availability is low, the amount of photosynthate rather decreases because dark respiration rate is also higher. Therefore, it is hypothesized that the higher L/R might be due to lower nitrogen demand for maximizing growth rate than in high-light environments, not due to capturing more light by increasing leaf area at the sacrifice of root growth in low-light environments. Since N area is determined by L/R, leaf mass per unit area, and root nitrogen uptake capacity, we are able to estimate optimal L/R and N area which maximize whole plant growth rate by considering above-mentioned plant traits. In this study we developed a biomass allocation model based on that of Osone and Tateno (2003) to demonstrate whether the L/R and N area in a low-light environment are optimized to maximize relative growth rate (RGR). Leaf (leaf mass per area and photosynthesis) and root (nitrogen absorption) properties were incorporated into the model. We also estimated the leaf net assimilation rate (NAR; g m−2 d−1) in various light environments to clarify relationship between light availability and nitrogen demand. There, N area and photosynthetic parameters were associated with actual meteorological data measured throughout the growth period. Using the N area - NAR relationship, we could predict the optimal L/R and N area in various light environments. Two pioneer trees, Morus bombycis and Acer buergerianum, were used for testing the model predictions. Finally, we discuss the biomass allocation strategy in a low-light environment from a whole-plant perspective.

Materials and Methods

Plant materials and experimental design

Experiments were conducted at the Nikko Botanical Gardens of the University of Tokyo (139°360′E, 36°450′N, 650 m a.s.l.). The mean air temperature was 12°C, and the annual precipitation was 2100 mm.

We used 1-year-old seedlings of mulberry tree (<span class="Species">Morus bombycis Koidz.) and <span class="Species">Trident maple tree (Acer buergerianum Miq.). These are typical pioneer deciduous trees in East Asia, which change their morphological and physiological traits largely. Seedlings grow fast because leaves flush sequentially and root growth continues throughout the growing season.

<span class="Species">Morus bombycis seeds were collected from a wild <span class="Species">M. bombycis tree in Nikko city in 2007. The seedlings were grown in plastic pots in an open field in 2007 and used for experiments from April to August 2008. One-year-old A. buergerianum seedlings which were grown in natural open environments were purchased from a nursery (Kairyoen, Saitama, Japan). They used for experiments from July to September 2009. Initial pot size was about 3 liter and seedlings were further transplanted carefully to 10 liter pots according to root size. Until just before the experimental period, those seedlings were placed in shade houses which were made of greenhouse frames and shade cloths. Relative photosynthetic flux density (RPPFD) in the shade houses was about 10% (measured by two quantum sensors, LI-1000, Li-Cor, Lincoln, NE, USA).

At the beginning of the experimental period, the main stem of each seedling was cut, and only one shoot was allowed to grow. Then, half of the seedlings were placed in the open field and the rest were in the shade houses, respectively. Pots were placed separately to avoid mutual shading. They were also grouped into two nutrient conditions with different <span class="Chemical">nitrogen concentrations. Other than N, these solutions contained the following: 3 mM <span class="Chemical">K2HPO4, 1 mM MgSO4·7H2O, 3 mM CaCl2, 25 µM H3BO3, 2 µM MnSO4·5H2O, 2 µM ZnSO4·7H2O, 0.5 µM CuSO4·5H2O, 0.5 µM Na2MoO4·2H2O, and 20 µm Fe-EDTA [16]. NH4NO3 was added to this solution and adjusted to 20 or 2 mM. Pot seedlings were grouped into four treatments: high-light condition and nitrogen-rich (HR) or nitrogen-poor (HP), and shade condition and nitrogen-rich (SR) or nitrogen-poor (SP). The nutrient solutions were applied to the seedlings every second day, and the seedlings were watered every day during the experiments.

Measurements and parameters

During the experimental period, PPFD (µ mol m−2 s−1) and air temperature (T a, °C) were measured at the experimental site every minute in both 2008 (Item No. 3668 for PPFD, Item No. 3667 for air temperature, Spectrum Technology, Ft. Worth, TX, USA) and 2009 (S-LIA-M003 for PPFD, S-THA-M006, for air temperature, Onset Computer, Pocasset, MA, USA).

In August 2009 we also measured the leaf temperature (T L, °C) of pot-grown maple leaves using thermocouples (TC6-T, Onset) because leaf temperature affects the dark respiration rate temperature dependency. We constructed an estimation equation for T L using multi-regression analysis and the PPFD and T a values.

Leaf photosynthesis was measured to determine the relationship between leaf <span class="Chemical">nitrogen content per area (N area) and the parameters of the light-photosynthesis curve using a portable photosynthesis measurement system (CIRAS1, PP Systems, Hitchin, Herts, UK). Pot seedlings from all four treatments were used for the measurements. The measurement conditions were as follows: <span class="Chemical">CO2 concentration, 400 µmol mol−1; leaf temperature, 25°C; and relative humidity, 50%. The maximum photosynthetic rate was measured at 1000 µ mol m−2 s−1 for the sun-exposed leaves (100%RPPFD) and at 200 µ mol m−2 s−1 for the shade leaves, so as not to cause photoinhibition. We also measured temperature dependency of photosynthetic rate by changing leaf temperature and irradiance variously. After the measurements, total nitrogen content of the leaves were measured for evaluating N area by a carbon-nitrogen (CN) analyzer (Vario EL, Elementar Analyzensysteme GmbH, Hanau, Germany).

Sampling

<span class="Species">Morus bombycis were harvested in mid-April and mid-August of 2008, and <span class="Species">A. buergerianum were harvested in early July and early September. Final biomass of the seedlings became much larger than initial biomass. Seedlings seemed not to be self-shaded because they had only one shoot per individual. At each harvest, four to ten seedlings per treatment group were sampled and divided into leaves, stems, and roots. After measuring the leaf area, each part of the seedlings was oven-dried at 80°C for more than 4 days. The samples were then weighed, and nitrogen content was measured with the CN analyzer.

Calculation

<span class="Chemical">Nitrogen absorption rates per unit root dry mass (SAR; gN g−1 d−1) were calculated considering the difference in total <span class="Chemical">nitrogen content and root dry mass between the two harvests following Osone & Tateno (2003). Changes in root dry mass were assumed to be exponential between harvests. The leaf mass per area (LMA; g m−2), L/R, and leaf mass per shoot mass (P Leaf; g g−1) were also determined for each treatment group and applied for model prediction.

The models

First, we developed an optimal growth model that predicts the optimal biomass allocation ratio and leaf <span class="Chemical">nitrogen content under various irradiance levels. The structure of the model was fundamentally based on that described by Osone and Tateno (2003).

In our model, the N area –<span class="Chemical">NAR relationship was used as the plant growth indicator. <span class="Chemical">NAR was estimated using an actual PPFD and photosynthetic light-response curve in which the temperature dependency of the photosynthesis and dark respiration rate were considered.

Net photosynthetic rate at certain PPFD (I) and leaf temperature (T), A(I,T), was expressed as follows:where A(I,T) is the gross photosynthetic rate at I and T and R(T) is the leaf dark respiration rate at T L (°C). A(I,25) is measured and expressed as the photosynthetic light-response curve which is a non-rectangular hyperbola:where A max is the light-saturated rate of gross photosynthesis, ϕ is the initial slope of the light-response curve, θ is the convexity of the light-response curve. A max and R d(25) are expressed as a function of N area, and ϕ and θ are assumed to be constant.

The temperature dependency of gross photosynthetic rate was incorporated as a function of T. A(I,T) was expressed as an quadric approximation formula where A(I,25) was relativized to 1 as a standard value:where a, a and a were constant values and obtained from the photosynthesis measurements for each species.

The temperature dependency of R(T) is described as [17]:where R d(T L) and R d(25) are values of R d at T L (°C) and 25°C, respectively. R is the gas constant (0.0083 J K−1 mol−1) and ΔH a is the activation energy of R d (66.405 kJ mol−1) [18]. We also considered the Kok effect by which the dark respiration rate decreases when leaves are exposed to sunlight [19], [20]: where eqn. 5 and eqn. 6 represent the dark respiration rate during the day and night, respectively.

The leaf photosynthesis parameters (A max, R d, θ, and ϕ) at 25°C were described following Hikosaka et al. (1999). Relationship between A max – N area relationship and R d – N area relationship were expressed as: where b 1, b 2, and b 3 were maximum rate of A max, x-intercept of the curve, and a constant that determines initial slope of the A max – N area relationship, respectively, and b 4 and b 5 were the slope and y-intercept of the R d – N area relationship. These parameters were obtained from the photosynthesis measurements for each species.

For a given N area, NAR was calculated by substituting the light dataset into above equations (eqn. 1 to 6), integrating A(I), converting CO2 to carbohydrate (1/6C6H12 O6), multiplying a transform coefficient of assimilated carbohydrate to the structural carbohydrate, and dividing the integrated A(I) by the growth period (day). The transform coefficient was found to be about 0.4, in which both construction and maintenance costs of leaves, stems, and roots were considered [21]–[23]. We also estimated NAR in low-light environments using datasets with PPFD reduced to 10% and repeated the above processes.

We determined optimal plant property values using the N area –<span class="Chemical">NAR relationship and an optimal biomass allocation model based on that of Osone and Tateno (2003).The model plant consisted of three parts: the leaf, stem, and root. The whole plant biomass (W) is expressed as:where W L, W S, and W R are the leaf, stem, and root biomass, respn>ectively. Leaf area (L A) is expressed as:where <span class="Chemical">LMA is the leaf mass per area (g m−2), a constant determined in each light environment.

Leaf <span class="Chemical">nitrogen content per biomass (N L) is different from stem and root <span class="Chemical">nitrogen content per biomass (N S and N R) and they are highly correlated. Because these relationships affect the prediction of optimal biomass allocation (Osone & Tateno 2003), we defined N S and N R as functions of N L as follows: where c 1, c 2, c 3, and c 4 are constant values. By introducing these relationships, absorbed nitrogen is partitioned into leaf, stem, and root correctly.

Leaf <span class="Chemical">nitrogen content per leaf area (N area) is expressed as:Plant biomass production per day is a product of net assimilation rate (<span class="Chemical">NAR) and L A:Newly produced biomass was first divided between shoot and root following Hilbert (1990) using the allocation coefficient P Shoot, which is shoot biomass per total biomass. Then, newly shoot biomass is further partitioned into the leaf and stem according to P Leaf, which is leaf biomass per shoot biomass, following Osone & Tateno (2003). Because there was almost no variation in P Leaf during the growth period for each species growing in each light environment, we only have to estimate the effect of P Shoot in the model simulation. Thus, the new biomass increment for each organ is expressed as: where 0