DOI: 10.5061/dryad.brv15dvj0
title: Balancing the nutrient needs: Optimizing growth in Malus sieversii seedlings through tailored nitrogen and phosphorus effects

description: The impact of nitrogen (N) and phosphorus (P) on the physiological and
 biochemical processes crucial for tree seedling growth is substantial.
 Although the study of plant hydraulic traits in response to N and P is
 growing, comprehensive research on their combined effects remains limited.
 Malus sieversii, a key ancestral species of modern apples and a dominant
 species in Xinjiang's Tianshan wild fruit forest, is witnessing a
 decline due to climate change, pests and diseases, compounded by
 challenges in seedling regeneration. Addressing this, a four-year study
 was conducted to determine the optimal fertilization method for it. The
 experiment explored varying levels of N (N10, N20, N40) and P (P2, P4,
 P8), and their combined effects (N20Px: N20P2, N20P4, N20P8; NxP4: N10P4,
 N20P4, N40P4), assessing their impact on gas exchange, hydraulic traits,
 and the interplay among functional traits in Tianshan Mountains' M.
 sieversii seedlings. Our study revealed that all nitrogen treatments
 enhanced gas exchange, while phosphorus addition negatively impacted it.
 N10 significantly increasing leaf hydraulic conductivity. All
 phosphorus-inclusive fertilizers adversely affected hydraulic
 conductivity. P8, N20P4 and N20P8 notably increased seedlings'
 vulnerability to embolism. Seedlings can adaptively adjust multiple
 functional traits in response to nutrient changes. The research suggests
 N10 and N20 as the most effective fertilization treatments for M.
 sieversii seedlings in this region, while fertilization involving
 phosphorus is less suitable. This study contributes valuable insights into
 the specific nutrient needs of it, vital for conservation and cultivation
 efforts in the Tianshan region.
description: Select sun-oriented, fully expanded leaves. Gas exchange
 parameters, including net photosynthetic rate (Pn), stomatal conductance
 (Gs), transpiration rate (Tr), and intercellular CO2 concentration (Ci),
 were measured using a Li-Cor 6400 portable photosynthesis system under a
 photosynthetic photon flux density of 1500 μmol m<sup>-2</sup>
 s<sup>-1</sup> between 9:00-13:00 BST. The leaf chamber used
 was a 2×3 cm unit with a red and blue light source. CO2 concentration was
 set to 400 ppm with a high flow rate of 500 μmol
 s<sup>-1</sup> to minimize stabilization time for Pn and Gs
 measurements.  Predawn water potential
 (Ψ<sub>Pd</sub>) and the midday water potential
 (Ψ<sub>M</sub>) were measured at predawn (5:00–7:00) and
 midday (13:00–14:00) using a pressure chamber (Model 3500, PMS Instrument
 Company, Albany, USA). The evening before measurements, select and mark
 healthy, north-south oriented branches with colored tape. The next day,
 measure the water potential of the marked branches. Place the cut branches
 in the pressure chamber, open the control valve, gradually increase
 pressure until liquid appears at the cut surface, then close the valve and
 record the water potential. In August 2020, we selected
 one healthy sample plant from each plot and marked them. In the evening,
 when transpiration tension is lowest, cut sun-facing branches about 50 cm
 underwater. Transport the branches back to the laboratory and store them
 at low temperatures, keeping them immersed in water. Cut the branches into
 25 cm stems, removing 2-3 cm from both ends. Use a xylem hydraulic
 conductivity and embolism measurement system (Xylem embolism meter,
 Bronkhorst, Montigny-les-cormeilles, France) to measure initial hydraulic
 conductivity (K<sub>0</sub>) and maximum hydraulic
 conductivity (K<sub>max</sub>). K<sub>0</sub>
 represents the xylem's hydraulic conductivity
 (K<sub>h</sub>). Measure K<sub>h</sub> using
 degassed pure water at low pressure (0.5-1.0 kPa) through a 0.45 μm filter
 until a stable flow rate is achieved. Calculate specific hydraulic
 conductivity (Kw) by normalizing Kh to the branch cross-sectional area
 (SA) and leaf-specific hydraulic conductivity (Kl) by normalizing
 K<sub>max</sub> to the total leaf area (LA).
 Flush the catheter with the same solution three times at high
 pressure (0.2 MPa) until hydraulic conductivity stabilizes. Then, measure
 the xylem's hydraulic conductivity again at low pressure (0.5-1.0
 kPa) to determine maximum hydraulic conductivity
 (K<sub>max</sub>). Gradually increase the branch xylem tension
 using an external cavitation chamber (PMS Model 3500, PMS Instrument
 Company, Albany, USA), maintaining each pressure level for 5 minutes.
 Immerse the branches in water and measure hydraulic conductivity (Kh).
 Continue adjusting the pressure gradient and measuring until hydraulic
 conductivity reaches 0.Create a xylem embolism vulnerability curve for
 plants based on varying pressures and the corresponding loss of hydraulic
 conductivity at these different pressure levels.
description: # Balancing the nutrient needs: Optimizing growth in Malus sieversii
 seedlings through tailored nitrogen and phosphorus effects
 [https://doi.org/10.5061/dryad.brv15dvj0](https://doi.org/10.5061/dryad.brv15dvj0) All data in this dataset were obtained from field measurements in the field through in situ experiments. The hydraulic trait and gas exchange data for the NxP4 addition were not included in this study. The notation "n/a or null" is used in the dataset to indicate the portions where data is missing. ## Description of the data and file structure The nutrient addition patterns that emerged from the data are specifically described as follows： | Treatments | N (g m-2 yr-1) | P (g m-2 yr-1) | Nutrient Description | | :--------- | :------------- | :------------- | :------------------- | | CK | 0 | 0 | Control group | | N10 | 10 | 0 | Low N addition | | N20 | 20 | 0 | Medium N addition | | N40 | 40 | 0 | High N addition | | P2 | 0 | 2 | Low P addition | | P4 | 0 | 4 | Medium P addition | | P8 | 0 | 8 | High P addition | | N20P2 | 20 | 2 | Medium N and low P | The terms and their meanings in the dataset are as follows： | Traits | Abbreviation | Units | | :-------------------------------------------------------- | :----------- | :--------------- | | **Gas exchange parameters** |   |   | | Net photosynthetic rate | Pn | μmol m⁻² s⁻¹ | | Stomatal conductance | Gs | mol m⁻² s⁻¹ | | Transpiration rate. | Tr | mol m⁻² s⁻¹ | | Intrinsic water use efficiency. | WUE | none | | **Hydraulic traits** |   |   | | Predown water potential | ΨPd | MPa | | Midday water potential | ΨM | MPa | | Diurnal water potential change | ∆Ψ | MPa | | Specific hydraulic conductivity (wood area normalization) | Kw | kg m-1 s-1 Mpa-1 | | Specific hydraulic conductivity (leaf area normalization) | Kl | kg m-1 s-1 Mpa-1 | | Percentage loss of conductivity | PLC | % | | Water potential at plant hydraulic conductivity loses 50% | P50 | Mpa | | **Others** |   |   | | Wood density | WD | g cm-3 | | Specific leaf area. | SLA | g cm-2 | ## Code/Software All the data used were analyzed by R4.2.2. Vulnerability curves were fitted and plotted using the R package ‘fitplc’ . The gas exchange parameters, hydraulic traits, specific leaf area, wood density and other phenotypic and physiological parameters of *M. sieversii* under different fertilization treatments were compared by one-way ANOVA and Turkey HSD method. We used Pearson correlation to analyze the coordination among hydraulic safety, hydraulic efficiency, gas exchange parameters and other physiological characteristics. The structural equation model was constructed using the ‘piecewiseSEM’ package in R, while the flowchart was created using the ‘ggalluvial’ package in R.