Radiation effects on nitrogen allocation coefficients for deciduous trees, evergreen trees, and herbaceous plants.

<p>We explore the reduced radiation from 800 to 400 <i>µmol</i> photon/<i>m</i>²/<i>s</i> on the proportions of nitrogen allocated to storage, carboxylation, electron transport, light capture and respiration for a leaf layer with prescribed functional nitrogen availability. Positive values indicate increase in nitrogen allocation while negative values indicate decrease in nitrogen allocation.</p

Main model inputs for three test cases.

<p>Note 1: <b><i>PAR</i></b> = photosynthetic active radiation for nitrogen allocation among carboxylation, light capture and electron transport (<i>µmol</i> photon/<i>m</i>²/<i>s</i>). Data for test case 1 and 2 is from the 10-km gridded data from the SUNNY model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Perez1" target="_blank">[53]</a> and NCEP/NCAR Reanalysis dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Kalnay1" target="_blank">[54]</a>, respectively, averaged for daytime period in July. Data for test case 3 is from the experimental controlled radiation. For shaded canopy locations in test case 2, the radiation level is calculated by multiplying the top of canopy radiation and the relative light (<i>x</i>) it receives.2: <b><i>DT</i></b><i>/</i><b><i>NT</i></b> = daytime temperature / nighttime temperature (°C). For test case 1, data are based on average daily minimum and maximum temperature in July from the DAYMET website <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Thornton2" target="_blank">[55]</a>. For test case 2, data are based on average daily minimum and maximum temperature in July from the NCEP/NCAR Reanalysis dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Kalnay1" target="_blank">[54]</a>. 3: <b><i>RH</i></b> = relative humidity, which is the ratio of the partial pressure of water vapor in the air to the saturated vapor pressure. Data are from the original papers. 4: <b><i>LMA</i></b> = Leaf mass per unit area (<i>g</i> /<i>m</i>²). For test case 1, <i>LMA</i> is calculated based on the mean values of old and new leaves in July <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Rogers1" target="_blank">[56]</a>. For shaded canopy locations in test case 2, the <i>LMA</i> is calculated based on the regression <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Niinemets5" target="_blank">[57]</a>: <i>y</i> = 73+65.5<i>x</i>, where <i>x</i> (0–1) is the radiation of leaf relative to the top of canopy. For the high growing temperature condition in test case 3, data is from Kobayashi et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Kobayashi1" target="_blank">[58]</a>. We assume a 20% increase in <i>LMA</i> at the low growing temperature given that the area based leaf nitrogen content increased by about 20% at the low growing temperature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Hikosaka2" target="_blank">[32]</a>. 5: is the proportion of net carbon assimilation allocated to leaf. We set to be 0.2 for test case 1–2 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-McCarthy1" target="_blank">[59]</a> and 0.6 for test case 3 based on fast-growing plants non-woody plants <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone.0037914-Poorter1" target="_blank">[60]</a>. 6: <b><i>MLNC_m</i></b> = Mean leaf nitrogen content (<i>g</i> N/<i>g</i> leaf).</p

Temperature effects on nitrogen allocation coefficients for deciduous trees, evergreen trees, and herbaceous plants.

<p>We explore the effect of increased growing temperature from 15°C to 20°C on the proportions of nitrogen allocated to storage, carboxylation, electron transport, light capture and respiration for a leaf layer with prescribed functional nitrogen availability. Positive values indicate increase in nitrogen allocation while negative values indicate decrease in nitrogen allocation.</p

Plant functional nitrogen availability optimization by linking a nitrogen allocation model and a nitrogen cost model.

<p>The rectangles represent state variables of interests. The shaded ones represent the target variables to achieve optimization/maximization. The hexagons represent models. Optimal plant functional nitrogen availability is achieved by maximizing the net carbon gain within a specified time period. The net carbon gain is based on the net carbon balance between photosynthesis and carbon cost of plant nitrogen maintenance and uptake. Nitrogen allocation model is used to predict the photosynthesis parameters (<i>V_c,max</i> and <i>J_max</i>) for the Farquhar photosynthesis model.</p

Fitted parameter values for test cases 1–3 using Metropolis-Hasting approach.

<p><b>Note:</b> Values in the parenthesis represent the standard deviation of the fitted parameter value.</p><p>Only control condition data are used for fitting the model.</p

Effects of elevated CO₂ concentration on nitrogen allocation coefficients for deciduous trees, evergreen trees, and herbaceous plants.

<p>We explore the effects of increased CO₂ concentration from 370 <i>ppm</i> to 570 <i>ppm</i> on the proportions of nitrogen allocated to storage, carboxylation, electron transport, light capture and respiration for a leaf layer with prescribed functional nitrogen availability. Positive values indicate increase in nitrogen allocation while negative values indicate decrease in nitrogen allocation.</p

<i>V_c,max25</i> (<i>V_c,max</i> scaled to 25°C) and nitrogen allocation coefficients for deciduous trees, evergreen trees, and herbaceous plants for the control case (temperature = 15°C; radiation = 800 <i>µmol</i> photon/<i>m</i>²/<i>s</i>; CO₂ = 370 <i>ppm</i>) in the sensitivity analysis.

<p>The nitrogen allocation coefficients are estimated with the nitrogen allocation model using parameter inputs from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone-0037914-t003" target="_blank">Table 3</a>.</p

Acclimation of <i>V_c,max25</i> (<i>V_{c, max}</i> scaled to 25°C) to altered environmental conditions.

<p>The nitrogen allocation model is used to quantify the responses of <i>V_c,max25</i> to increased growing temperature (from 15°C to 20°C; labeled as “T” in the figure), increased CO₂ concentration (from 370 <i>ppm</i> to 570 <i>ppm</i>; labeled as “CO₂” in the figure) and reduced radiation (from 800 to 400 <i>µmol</i> photon/<i>m</i>²/<i>s</i>; labeled as “RAD” in the figure) and their interactions for generic deciduous trees, evergreen trees, and herbaceous plants. The change of <i>V_c,max25</i> to environmental conditions in our model results from a decrease or an increase in nitrogen allocation to carboxylation. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone-0037914-g002" target="_blank">Figure 2</a> for <i>V_c,max25</i> and nitrogen allocation coefficients for the control case. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037914#pone-0037914-t003" target="_blank">Table 3</a> for main input parameters of the model.</p

Hierarchical plant functional nitrogen allocation for a leaf layer of a tree.

<p>The leaf layer is assigned with a certain amount of plant functional nitrogen (<i>FNA_a</i>) required to support its growth and maintenance. The required plant functional nitrogen includes the functional nitrogen in leaves as well as the functional nitrogen in roots and sapwood, which is used to acquire water and nutrient for photosynthesis and to provide nitrogen for new tissue synthesis using the photosynthetic products. Structural nitrogen is associated with functional nitrogen to build structural components (DNA and cell walls) in tissues of leaves, sapwood and roots. The available functional nitrogen is first divided into growth nitrogen and storage nitrogen. The growth nitrogen is further divided into photosynthetic nitrogen and respiratory nitrogen, with the photosynthetic nitrogen divided into nitrogen for light harvesting and nitrogen for carboxylation (nitrogen in Calvin Cycle enzymes). Finally, nitrogen allocated for light harvesting is divided into nitrogen for light capture (nitrogen in proteins of phosystems I, II and chlorophyll a/b complexes) and nitrogen for electron transport (nitrogen in proteins of thylakoid bioenergetics). The parameter in the parenthesis indicates the proportion of nitrogen invested for its category in the same row.</p

Molecular Insights into Arctic Soil Organic Matter Degradation under Warming

Molecular composition of the Arctic soil organic carbon (SOC) and its susceptibility to microbial degradation are uncertain due to heterogeneity and unknown SOC compositions. Using ultrahigh-resolution mass spectrometry, we determined the susceptibility and compositional changes of extractable dissolved organic matter (EDOM) in an anoxic warming incubation experiment (up to 122 days) with a tundra soil from Alaska (United States). EDOM was extracted with 10 mM NH₄HCO₃ from both the organic- and mineral-layer soils during incubation at both −2 and 8 °C. Based on their O:C and H:C ratios, EDOM molecular formulas were qualitatively grouped into nine biochemical classes of compounds, among which lignin-like compounds dominated both the organic and the mineral soils and were the most stable, whereas amino sugars, peptides, and carbohydrate-like compounds were the most biologically labile. These results corresponded with shifts in EDOM elemental composition in which the ratios of O:C and N:C decreased, while the average C content in EDOM, molecular mass, and aromaticity increased after 122 days of incubation. This research demonstrates that certain EDOM components, such as amino sugars, peptides, and carbohydrate-like compounds, are disproportionately more susceptible to microbial degradation than others in the soil, and these results should be considered in SOC degradation models to improve predictions of Arctic climate feedbacks