Densification Yellow Poplar Summary

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Abstract The densification of yellow poplar (Liriodendron tulipifera) has emerged as a critical area of research, driven by its desirable properties and broad potential applications. This study investigated the effects of four densification parameters using a 24 full factorial design to evaluate their impact on physical and mechanical properties. Analysis of variance (ANOVA) and Pareto analyses identified the compression ratio as the most influential factor, significantly affecting bending strength, compression strength, hardness, and spring-back behavior. Pressing temperature was the second most significant factor, with higher levels positively impacting mechanical properties. However, increasing the pre-steaming treatment duration from 30 to 60 min at 130 °C had a detrimental effect on strength and spring-back performance, particularly at a 50% compression ratio. Pressing time showed no significant effect on strength properties but contributed positively to hardness and spring-back behavior at higher levels. Several significant factor interactions were observed, further influencing the outcomes. Differences in density profiles were notable across compression ratios, with higher ratios producing more uniform distributions. Under optimal parameters, densification increased compression strength by 117%, bending strength by 60%, and hardness by 154% compared to undensified control samples, demonstrating the potential of densified yellow poplar (Liriodendron tulipifera) for high-performance applications. Keywords: densification; yellow poplar; factorial design; density profile; mechanical properties; dimensional stability; hydro-thermo mechanical treatments

1. Introduction Wood density is generally directly proportional to its strength, durability, and overall performance. Denser wood exhibits superior load-bearing capacity, making it an excellent choice for structurally demanding applications such as beams, columns, and flooring. Under certain circumstances higher wood density can also contribute to improved dimensional stability, but the relationship depends on various factors. As is well known, dimensional stability refers to a wood’s ability to resist changes in size and shape due to moisture content fluctuations. Densification decreases the wood’s porosity, reducing its capacity to absorb and release moisture; this can improve dimensional stability by minimizing swelling and shrinkage [1,2]. Dense wood products, therefore, will be more reliable and long-lasting, which can lower maintenance costs and minimize the need for replacements over time. Yellow poplar (Liriodendron tulipifera) is a fast-growing hardwood species native to North America, known for its tall, straight trunk and unique, tulip-shaped leaves. Its fine, uniform grain contributes to excellent workability, making it a favored material for furniture, cabinetry, and millwork. Additionally, its lightweight nature and ease of machining make it accessible and versatile for various applications. However, its relatively low density and hardness limit its utility in more demanding structural or wear-resistant applications. To address these limitations, densification techniques have been employed to improve yellow poplar (Liriodendron tulipifera)’s performance including thermal, mechanical, and chemical processes [1,2,3]. Chemical densification processes often require harsh chemicals that can be harmful to the environment if not managed properly. These substances can produce hazardous waste, release volatile organic compounds (VOCs), and require additional energy for handling and disposal. Chemicals used in densification can pose risks to workers during processing and to consumers if any residues remain in the finished product. Also, a chemical treatment may alter the appearance, texture, and natural feel of the wood. In contrast, environmentally friendly methods such as thermo-hydro-mechanical (THM) densification combine compression with heat and steam to increase the wood’s density, hardness, and dimensional stability. This method modifies the wood’s microstructure, collapsing cell walls and reducing porosity, which enhances mechanical properties like modulus of elasticity and rupture [1]. Heat treatment at controlled temperatures further stabilizes the compressed wood, reducing spring-back and improving resistance to environmental moisture. A THM method has a lower ecological footprint, aligns better with sustainability goals, and meets stricter environmental regulations, as seen in many countries increasingly banning or limiting VOC emissions and hazardous substances. This makes green densification methods more favorable for manufacturers aiming to adhere to international environmental standards and certifications like the Forest Stewardship Council (FSC) or Leadership in Energy and Environmental Design (LEED) [4]. Environmentally friendly densification methods tend to require less energy and fewer resources compared to chemical processes, which need additional steps for chemical application, curing, and waste management. This can result in cost savings and greater efficiency for manufacturers, especially as eco-friendly technologies advance [5]. Most of the wood densification using a hydrothermal process has been performed at temperatures higher than 140 °C [6,7,8,9,10]. Yin et al. [6] investigated the impact of high-temperature steam treatment on spruce wood cell walls, focusing on changes in chemical and mechanical properties. The study used FT-IR microscopy and nanoindentation to examine modifications in the cell wall’s biopolymer matrix, specifically in celluloses, hemicelluloses, and lignin, at steam treatment temperatures of 140 °C, 160 °C, and 180 °C. According to their findings, steam treatment caused the degradation of hemicellulose components, with significant changes observed at 160 °C and above. There was a breakdown of carbonyl groups in xylan and glucomannan, and a loss of specific chemical groups in lignin. These modifications decreased the wood’s capacity to absorb moisture, enhancing dimensional stability. Also, higher steam temperatures (160–180 °C) reduced the wood’s hygroscopicity by diminishing the accessible hydroxyl groups, making it less prone to water absorption. The authors concluded that steam treatment at high temperatures can enhance wood’s dimensional stability and reduce hygroscopicity but may also compromise its mechanical strength due to the degradation of biopolymer linkages within the cell wall. Kutnar and Kamke [7] investigated how varying temperature and steam conditions affect the set recovery (spring-back) of densified poplar wood. The study examined densification treatments using saturated steam, superheated steam, and transient conditions at temperatures of 150 °C, 160 °C, and 170 °C, with post-treatment at 200 °C to improve dimensional stability. The study found that densification under saturated steam at 170 °C minimized set recovery, especially with post-treatment at 200 °C for up to 3 min. The best results showed only 6% set recovery after five wet/dry cycles. Higher treatment temperatures generally increased the wood’s oven-dry density and reduced its equilibrium moisture content, contributing to greater dimensional stability. The highest density was achieved with saturated steam treatment at 170 °C, followed by 200 °C post-treatment. Bending tests indicated that the post-treatment at 200 °C for up to 3 min had no significant effect on these mechanical properties, though repeated wet/dry cycles reduced the Modulus of Rupture (MOR) and Modulus of Elasticity (MOE). The study by Zhou et al. [9] investigated the surface densification of poplar wood (Populus tomentosa Carr.) through a thermo-hydro-mechanical (THM) process, focusing on the effects of processing parameters such as compressing temperature, closing speed, and compression ratio. Their conclusions indicated that a compressing temperature of 140–160 °C, closing speed of 10 mm/min, and compression ratio of 20% were recommended to achieve effective surface densification on their wood samples. In another work, Xiang et al. [10] explored how superheated steam and densification improve the properties of poplar wood. The study focused on creating two types of sandwich-densified wood, which differ based on the densified layer’s position, through controlled densification and superheated steam treatment. Treatment at 0.1 and 0.3 MPa improved dimensional stability by reducing moisture absorption and swelling. Although superheated steam darkened the wood and slightly reduced its hardness and MOR, the treatment effectively stabilized the wood against shape recovery. The superheated steam-treated sandwich-densified wood exhibited properties like hardwood flooring materials, showing promise for use in solid wood flooring with improved stability and reduced environmental impact. Shao et al. [11] studied also the effects of thermo-hydro-mechanical treatments on the physical and mechanical properties of poplar wood. They used temperatures of 180 °C, 200 °C, and 220 °C and they determined that higher treatment temperatures and longer durations degraded mechanical properties due to the thermal decomposition of chemical components. Rahimi et al. [12] investigated the impact of hydrothermal treatments, specifically steam and hot compressed water (HCW) at temperatures of 100 °C and 140 °C, on the physical properties and drying behavior of yellow poplar (Liriodendron tulipifera) heartwood as an alternative to produce chemical-free wood products with enhanced properties. The findings suggested that hot-compressed water treatment at a moderate temperature holds promise for enhancing the properties of wood, providing insights for sustainable and energy-efficient alternatives in wood processing. Considering the importance of the topic and the preliminary work performed, the primary goal of this study was to investigate the mechanical properties of hydro-thermo-densified yellow poplar (Liriodendron tulipifera) wood under varying compression ratios, pre-steaming times, pressing temperatures, and pressing durations. All factors were tested at two levels using a 24 full factorial experimental design for a comprehensive analysis. In each of the 16 experimental setups, the density profile, compression strength parallel to the grain, modulus of rupture (MOR), hardness, and spring-back behavior were determined. The main and second-order interaction effects were evaluated using analysis of variance (ANOVA) and Pareto analyses, providing a detailed understanding of each factor’s influence and interactions on the measured properties.
2. Materials and Methods For the experiments, yellow poplar (Liriodendron tulipifera) boards were sourced from the Northeastern Appalachian region in West Virginia. The boards were kiln-dried at the sawmill to an equilibrium moisture content of approximately 7% and planed to dimensions of 23 mm × 178 mm × 3048 mm (T × W × L). From these boards, 224 defect-free specimens with tangential annual ring orientation were prepared, each measuring 305 mm × 19 mm × 19 mm in the longitudinal (L), tangential (T), and radial (R) directions, respectively. Prior to densification, the specimens were conditioned in a laboratory environment at a temperature of 23 °C ± 2 °C and relative humidity of 60% ± 3% to ensure a 9% equilibrium moisture content (EMC) across all over the specimens. The moisture content of the samples was measured following the ASTM D4442-20 Method B [13] procedures. 2.1. Densification Method During the densification process, four factors (A–D) were controlled, and the factor levels used in a factorial design are presented in Table 1. The table contains the factors’ real value, and for the regression analysis we used the factors’ coded values (−1) for the low level, and (+1) for the high level. Table 1. The four controlled factors and their levels.

The applied compression ratio (CR), which specifies the reduction in specimens’ thickness, was calculated using Equation (1). 𝐶𝑅=⎡⎣⎢(𝑡𝑖−𝑡𝑓)𝑡𝑖⎤⎦⎥×100 [%] (1) where ti is the initial thickness of the specimens, and tf is the final target thickness. Sixteen experiments were performed as outlined in Table 2, with fourteen randomly selected specimens used for each densification experiment. Additionally, fourteen pieces with the same dimensions were prepared for the undensified control samples. Table 2. Experiment design.

The experimental setups according to the four-factor at two levels full factorial experiment design (24) are presented in Table 2. The design was replicated once and all 16 setups were randomly repeated, resulting in two batches of specimens for each setup. The densification process began with a pre-steaming plastification treatment in an autoclave, using saturated steam at a temperature range of 120–130 °C. Immediately after the steaming, the specimens were placed in a hydraulic hot press. A Carver 38952NE1 hydraulic press was used, with both press plates heated to the specified temperature. As shown in Figure 1, the target thickness of the samples was ensured by placing three metal spacers between the press plates, preventing physical over-compression of the specimens. Forests 16 00323 g001 Figure 1. During the hot press stage with a 20% compression ratio. Metal spacers are shown in the picture. After the set pressing time, the specimens were removed from the hot press and cooled down at room temperature. 2.2. Determination and Evaluation of Density and Density Profiles The density of the specimens was measured before and after the densification process in accordance with the ASTM D2395-17 Test Method B [14] procedures. Mass and volume measurements were conducted at an equilibrium moisture content (EMC) of 9% ± 1% for all specimens to ensure comparability between the initial and final densities. The mass and dimensions (longitudinal, tangential, and radial) of each sample were measured with an analytical balance (±0.001 g) and a vernier caliper (±0.01 mm). The density (𝜌𝑀 ) in (kg/m3) was calculated using Equation (2): 𝜌𝑀=𝑚𝑀𝑉𝑀 (2) where mM is the mass at moisture content M and VM is the volume of specimens at moisture content M. The density distribution across the thickness of undensified and densified wood samples was determined using an X-ray densitometer (QMS, Model QDP-01, Quintek Measurement Systems, Inc., Knoxville, TN, USA). The density values were recorded at every 0.02 mm along the thickness. The following parameters of the density profile were determined (Figure 2): the maximum density in the middle section of the thickness (MDmax), the maximum density in both left and right surface layers (SDLmax; SDRmax), and the distance of the maximum density peaks from the respective surface (DL; DR). Two specimens from each experimental setup were subjected to density profile analysis, along with eleven undensified (control) specimens. For the control specimens, the maximum densities were identified, with MDmax representing the denser latewood density across the thickness. For each specimen, the average maximum density (SDmax.avg.) was calculated using Equation (3): 𝑆𝐷𝑚𝑎𝑥.𝑎𝑣𝑔=𝑆𝐷𝐿𝑚𝑎𝑥+𝑆𝐷𝑅𝑚𝑎𝑥2 (3) Forests 16 00323 g002 Figure 2. Density distribution profile of a densified specimen (example). The average distance of the maximum density peak (Davg.) from the respective surfaces was calculated using Equation (4): 𝐷𝑎𝑣𝑔.=𝐷𝐿+𝐷𝑅2 (4) Factorial analyses were not performed on the density profile data, since two specimens per experimental setup were insufficient to yield reliable results. Since the compression ratio emerged as the most influential factor for all examined mechanical properties of densified yellow poplar (Liriodendron tulipifera), the specimens were grouped into two categories based on the compression ratio. Specifically, the average maximum densities (SDmax.avg.) and the average distances of the density peaks (Davg.) from setups #1, #2, #5, #6, #9, #10, #13, and #14 were combined into the 20% compression ratio category. Similarly, data from setups #3, #4, #7, #8, #11, #12, #15, and #16 were combined into the 50% compression ratio category. 2.3. Evaluation of Spring-Back Behavior After the densification was performed, the thickness of the samples was immediately measured. Spring-back (SB) is defined as the relative difference between the thickness achieved immediately after densification and the target thickness; it was calculated using Equation (5): 𝑆𝐵 (%)=⎡⎣⎢(𝑡𝑓−𝑡𝑡)𝑡𝑡⎤⎦⎥×100 [%] (5) where tf is the final thickness measured immediately after the pressure release and tt is the target thickness for the compressed specimens. 2.4. Mechanical Tests The mechanical tests for control and densified specimens were performed using an Instron universal testing machine with the Bluehill 2 (version 2.6) universal testing software (Instron, Norwood, MA, USA). 2.4.1. Static Bending Test The bending test was conducted following the ASTM D143-14 standard (Section 8) [15]. The test is represented in Figure 3, using center-point loading at a test rate of 1.3 mm/min. The span value was 254 mm for the densified specimens and 266 mm for the control specimens, ensuring the required minimum span-to-depth ratio of 14. Tests were performed in the densified direction (tangential direction). The failure type was recorded for each specimen, and the modulus of rupture (MOR) in (MPa) was calculated using Equation (6): 𝑀𝑂𝑅=3×𝑃𝑚𝑎𝑥×𝐿2×𝑏×ℎ2 (6) where Pmax is the maximum force (N) at the mid-span, L is the span length (mm), b is the width (mm), and h is the depth (mm). Forests 16 00323 g003 Figure 3. Static bending test setup. Eight specimens were used for each experimental setup to determine the static bending strength of densified specimens, along with an equal number of control samples. 2.4.2. Compression Parallel to Grain Compression parallel to grain was performed according to ASTM D143-14 standard (Section 9). Figure 4 illustrates the setup of the compression tests. The test specimens had a length of 76 mm, and the load was applied at a rate of 0.003 mm/mm of nominal specimen length per minute. For each specimen, the maximum load was recorded. The compressive strength (σc) in (MPa) was calculated using Equation (7): 𝜎𝑐=𝑃𝑚𝑎𝑥𝐴 (7) where Pmax is the maximum force (N) and A is the cross-sectional area (mm2). Forests 16 00323 g004 Figure 4. Compression test parallel to the grain. Eighteen specimens were used for each experimental setup to determine the compressive strength parallel to the grain of densified specimens, along with an equal number of control samples. 2.4.3. Hardness Hardness was determined following ASTM D143 (Section 13) [15]. This is the standard test method for small clear specimens of timber, using a modified ball test with a ball diameter of 11.3 mm, as shown in Figure 5. The projected area of the ball on the test specimen was 1 cm2. Penetrations were performed on the densified (tangential) surface of each specimen. The testing speed was 6 mm per minute, and the load at which the ball penetrated to half its diameter was recorded. Forests 16 00323 g005 Figure 5. Hardness test setup. Eighteen specimens were used for each experimental setup to determine the hardness of densified specimens, along with an equal number of control samples. 2.5. Scanning Electron Microscopy (SEM) Samples were sputter-coated with an ultrathin layer of gold palladium for conductivity using a Denton Desk V sputter, from Denton Vacuum Co., Moorestown, NJ, USA. Then, they were imaged using a JEOL JEM7600F scanning electron microscope (JEOL USA, Inc., Peabody, MA, USA) at 5 KV. 2.6. Statistical Analyses A 24 full factorial experimental design was applied in this study, combining four factors at two levels, resulting in 16 experimental setups. The design was repeated one time, with additional repetitions within each experimental setup to ensure the reliable results for the analyses. Factorial analysis of variance (ANOVA) was employed to evaluate the results and identify the significant effects of the factors, while Pareto analysis was used to examine their relative contributions. Both main effects and second-level interactions were investigated. All statistical analyses were performed using TIBCO® Data Science/Statistica 13 software. ANOVA and the Student’s t-test were conducted with the significance level set at α = 0.05. The response surface methodology (RSM) was used to model mathematically the relationship between the dependent variables (factors) and optimize the response variables’ value. 3. Results and Discussion 3.1. Density and the Density Profile Table 3 presents the initial and final average density, the density increase, and the average thickness of the specimens across all 16 experimental setups. The density values were calculated using Equation (2). Table 3. The average density and thickness values across all 16 experimental setups.

As explained in Section 2, the samples in the density profile analysis were grouped by compression ratio, while the effects of the other three factors (pre-steaming, press temperature, and pressing time) on the density profile were reflected in the deviations of the results. Figure 6 shows the density profile of (a) two control specimens; (b) two samples from setup #1; (c) two samples from setup #7; and (d) two samples from setup #16. Forests 16 00323 g006 Figure 6. Density profile of (a) two samples of undensified samples, (b) two samples from setup #1, (c) two samples from setup #7, and (d) two samples from setup #16. Each line corresponds to an individual sample. The presented density profiles of the control samples (Figure 6a) exhibit noticeable differences. Density variations along the cross-section could be observed, which could be due to the density differences between earlywood and latewood. When the 20% compression ratio was applied (Figure 6b), density fluctuations were also observed beyond the densified surface in the middle section. Figure 7a,b show the SEM images of the specimens from setup #6. Forests 16 00323 g007 Figure 7. SEM images of setup #6 with 20% densification. (a) Magnification: 25×; (b) magnification: 250×. In Figure 7a, it can be observed that beyond the densified zone, the cell morphology did not change, suggesting that the density differences between earlywood and latewood caused these fluctuations. In addition, close to the specimens’ surface, a non-densified zone can be observed, which also appears on the density profile. Figure 7b captures the densified zone, where the extent of deformation and the bending of cell walls indicate that the wood exhibited viscoelastic behavior during compression. However, the presence of open cells suggests that the 20% compression ratio did not fully close the cell structure. When the 50% compression ratio was employed, the M-shaped density gradient became the most dominant. Kutnar et al. [16] observed a similar M-shape in the densification profile of hybrid poplar wood with a 63% degree of densification. At a 50% compression ratio, the density in the middle section became more uniform while achieving an elevated density value. Figure 8 illustrates the relative differences between the average maximum surface density and both the average initial density and the maximum density in the middle section of the sample. The initial density of the specimens was calculated using Equation (2), and the average maximum surface density (SDmax.avg) was computed using Equation (3). Forests 16 00323 g008 Figure 8. Relative differences between various types of average densities categorized by compression ratio. For the 20% compression ratio, the relative difference between the middle section and surface densities is 37.3%, which is significantly larger compared to the 50% compression ratio, where this difference is only 9.1%. This indicates a more uniform density distribution in the cross-section for the higher compression ratio studied. Additionally, the relative difference from the average initial density to the surface density (SDmax.avg) is 64% for a 20% compression ratio, compared to 102.3% for the 50% compression ratio, reflecting the greater surface density achieved at the higher compression ratio. Figure 9 shows the average maximum density for samples from both compression ratios and the control group. For the densified samples, the average maximum density represents the average value of the density peak near the surface (SDmax.avg) and was calculated using Equation (3). For the control samples, the maximum density represents the latewood density, which appears as a periodic peak on the density profile (MDmax). Forests 16 00323 g009 Figure 9. Average maximum density for samples from the two compression ratios and the control group. The densification process significantly increased the density of the samples compared to the control ones. The control samples exhibited an average maximum density of 554 kg/m3. With a 20% compression ratio, the average density increased by 37% (i.e., to 761 kg/m3), while at a 50% compression ratio, it rose by 76% (i.e., to 976 kg/m3). The 50% compression ratio resulted in a 28% increase in density compared to the 20% ratio. The distance between the average maximum density and the specimens’ surface (Figure 2), grouped by compression ratio, is presented in Figure 10. Forests 16 00323 g010 Figure 10. Average distance between the specimens’ surface and the average maximum density, categorized based on the compression ratio. For the 20% compression ratio, the average distance between the density peak and the surface was 1.15 mm. At the 50% compression ratio, the density peak shifted deeper into the wood, reaching 1.35 mm, representing a 17% increase in distance. However, as presented in Figure 10, there is no significant statistical difference between these two distances. A similar phenomenon was reported by Rautkari et al. [17], where the densification rate of Scots pine surfaces increased from 16.6% to 31.8%. Additionally, further density peak shifting was observed in specimens subjected to hydrothermal post-treatment. Laine et al. [18] also documented a density peak shift when Scots pine sapwood was compressed at a 25% compression ratio, with varying temperatures and pressing times. They attributed the deeper density peak to the viscoelastic behavior of wood. Another possible explanation is the degradation of the wood surface and the hydrolysis of hemicelluloses, particularly at higher compression ratios. These factors, combined with the migration of heat and moisture deeper into the wood, likely contribute to the further density peak shifting into the specimen. 3.2. Spring-Back Immediately after the press was opened, the thickness of each sample was measured to evaluate spring-back. Spring-back provides information about the immediate elastic recovery caused by the shape memory effect of the cells. This memory effect can pose a significant issue when densified wood is exposed to water or high-humidity environments. Figure 11 presents the spring-back results across the 16 experimental setups, categorized by the densification ratio. Forests 16 00323 g011 Figure 11. Average spring-back of the 16 experimental setups. The compression ratio influenced the spring-back significantly, leading to differences of up to one order of magnitude in some cases. A higher compression ratio, especially in the case of volume densification rather than surface densification, causes greater set-recovery or spring-back, as reported in several previous studies [19,20,21,22]. To achieve reliable statistical analysis, the original 24 full factorial design was divided into two separate 23 full factorial designs, excluding the compression ratio. This separation allowed for the independent analysis of the effects of pre-steaming time, press temperature, and pressing time within each compression ratio. Using the low-level (20%) compression ratio, setups #5, #6, #13, and #14 produced an average spring-back with negative values, indicating that the samples shrank due to the densification process. Across these four setups, only one factor—the pressing temperature—was set to the same level (high level). Among the 20% densified samples, setup #14 achieved the lowest average spring-back of −1.7%, whereas the highest value of 2.1% was observed with the factor parameters of setup #1. Since the target thickness was ensured by solid metal spacers in the press, once the press plates closed and touched the spacers, the samples began to dry out from the sides. This drying could result in a thinner final thickness than the target. Similarly, Balasso et al. [20] reported this phenomenon during the densification of Eucalyptus nitens, which resulted in an average spring-back value of −4.52%. With a high-level (50%) compression ratio, setup #15 produced the lowest average spring-back value of 5.3%, which was achieved with a low pre-steaming time and high levels of press temperature and pressing time. However, all the specimens showed a high standard deviation. Based on the Pareto’s chart (Figure 12a,b), the pressing temperature was the most influential factor, followed by the pressing time, and the pre-steaming time was the third most influential factor. Since the Standardized Effect Estimate (SEE) values were negative for the press temperature and pressing time across both compression ratios, increasing these factor levels can effectively reduce the spring-back recovery. Forests 16 00323 g012 Figure 12. Pareto chart of the effects on the spring-back. (a) 20% compression ratio, (b) 50% compression ratio. According to Figure 12, only the pre-steaming factor changed depending on the compression ratio. With a 20% compression ratio, longer pre-steaming (60 min) slightly contributes to reducing the spring-back ratio. However, at the 50% compression ratio, the SEE value is positive, indicating that longer pre-steaming has a significant detrimental effect on the spring-back value. Figure 13a–c presents the response surface plots of the spring-back. Based on the response surface inclination direction and color zones we can optimize the factors’ level setup for a designed response. Forests 16 00323 g013 Figure 13. Response surface plots of the spring-back related to specific variables. (a) Interaction between pressing time and pre-steaming time at a 20% compression ratio; (b) interaction between pressing time and press temperature at a 20% compression ratio; (c) interaction between pressing time and press temperature at a 50% compression ratio. 3.3. Bending Strength—Modulus of Rupture (MOR) Figure 14 presents the bending strength results of the 16 experimental setups and the control samples, measured according to the ASTM D143 [15] standard procedures. Forests 16 00323 g014 Figure 14. Average bending strength of the 16 experimental setups and control samples. Among all the experimental setups, setup #6 resulted in the lowest increase in the average MOR, showing only a 0.5% improvement compared with the control result of 90 MPa. The highest average MOR was obtained using the factor levels of setup #15, where the pre-steaming time was set low (30 min), the compression ratio was high (50%), the press temperature was high (150 °C), and the pressing time was also high (30 min). These experimental setups increased the average MOR value from the undensified 92.2 MPa to 147.9 MPa, representing a 60.4% increase. In addition to setup #15, three other setups (#7, #11, and #16) also yielded outstanding results, which did not differ significantly from each other, as determined by the Tukey HSD test. The common parameters across these setups were the high-level compression ratio and low-level pre-steaming time, except in the case of setup #16. The four significantly higher results closely align with the reported value (140 MPa) by Kutnar and Kamke [8], which was achieved when hybrid poplar wood was treated with 160 °C superheated steam for 3 min and pressed at a pressure of 5.5 MPa [8]. A similar result (MOR = 150 MPa) was reported by Bao et al. [19] when poplar wood was densified to a 75% densification ratio at a constant temperature of 160 °C for 20 min. Comparing the maximum value of 147.9 MPa with the published bending strength values of small defect-free samples in the Wood Handbook [23], it can be concluded that the densified wood considerably exceeds these values. As shown in Figure 15, the Pareto chart indicates that the compression ratio emerged as the most important factor affecting bending strength, with an SEE value of 5.3. Forests 16 00323 g015 Figure 15. Pareto chart of the effects on the bending strength. The pre-steaming time was the second most significant factor, with an SEE value of −3.86. Despite the relatively low temperature (120–130 °C) of the steam, the detrimental effect of longer steaming lies in the hydrolytic decomposition of hemicelluloses caused by water molecules in the steam. Although the samples had an initial moisture content of 9%, the steaming process increased their moisture content. Inoue et al. [24] reported a similar negative relationship between the bending MOR and increasing steaming time, even when significantly shorter steaming durations (a maximum of 8 min) at elevated temperatures (180–220 °C) were used. In addition to the main effects of the factors, the ANOVA revealed a significant effect of the 2by3 and 2by4 factor interactions. These interactions exhibit a spreading interaction type, meaning that the effect of the compression ratio is directly proportional to the levels of the pressing temperature and the pressing time. As shown by the response surfaces (Figure 16a,b), when both factors are at high levels, the positive effect is amplified. Forests 16 00323 g016 Figure 16. Response surface plots of the bending strength as response variable. (a) MOR in function of press temperature and compression ratio; (b) MOR in function of pressing time and compression ratio. The regressions model for the bending strength including the statistically significant main factors’ effect and two-way interactions: 𝑦𝑀𝑂𝑅=116.1−6.7𝑥1+9.2𝑥2+4.95𝑥3+4.67𝑥2𝑥3+3.78𝑥2𝑥4 𝑅2=0.35 where xi is the coded variable of the main factor effects (−1 ≤ xi ≤ +1); xixj are the coded variables of the factor’s two-way interactions, generated by multiplication of the main factor effects’ coded values; x1 represents the pre-steaming time (factor A), x2 represents the compression ratio (factor B), x3 represents the press temperature (factor C), x2x3 represents the BC factor interaction, and x2x4 represents the BD factor interaction (factor D is the pressing time). Setting the factors at their optimum level (x1 = −1; x2 = +1; x3 = +1) the model estimates the bending strength at the yMOR = 145.4 MPa value, which is close to the measured average MOR value for the experimental setup #15, where the factors are also set at their optimum values. The difference can be explained by the lower coefficient of determination, i.e., the fitted regression model represents a lower proportion of variance. A positive correlation between the MOR and compression rate was previously reported by Bao et al. [19]. However, the relationships with other manufacturing parameters have not been reported in previous studies. Although the pressing time was not significant as a main effect, its interaction with the compression ratio was found to be slightly significant (p = 0.03). 3.4. Compression Strength Parallel to Grain Figure 17 presents the compression strength results for the 16 experimental setups and the control samples, measured in accordance with the ASTM D143 (Section 9) [15] procedures. Forests 16 00323 g017 Figure 17. Average compression strength of the 16 experimental setups and control samples. Regardless of the densification parameters, the average compression strength of all the treated samples greatly exceeds that of the control samples, which is 38 MPa. Among the treated samples, only the average strength of 45 MPa in setup #2 did not differ significantly from the control samples at a 5% significance level. No significant difference was found between the lower average values of setups #1, #2, #9, and #10, as determined by the Tukey honestly significant difference (HSD) test. In all these cases, the compression ratio and pressing temperature were set at a low level. In contrast, when these factor levels were set at the high level, as in the case of setups #7, #15, and #16, the highest strength values were obtained. With a low steaming time, high pressing rate, high pressing temperature, and low pressing time, setup #7 achieved an average compression strength of 82.3 MPa, which is 116.6% greater than that of the 38 MPa control samples. The key role of the compression ratio and press temperature was confirmed via ANOVA and Pareto analyses. Figure 18 shows the Pareto chart with the Standard Effect Estimate (SEE) values for the four main effects and the two-level interactions. Forests 16 00323 g018 Figure 18. Pareto chart of the effects on the compression strength. The steaming time also significantly affected the compressive strength values, whereas the pressing time did not significantly affect the compressive strength values. Based on the Pareto analyses, the high level of setup parameters for both the compression ratio (50%) and the press temperature (150 °C) was beneficial; however, the longer pre-steaming time (60 min) was detrimental to the strength performance. The strongest interaction can be observed between the compression ratio and the pressing temperature. The positive “standardized effect estimate” (SEE) of these interactions indicates that increasing both factors simultaneously leads to higher strength values, as also demonstrated by the response surface (Figure 19a). Forests 16 00323 g019 Figure 19. Response surface of the factor interactions with compression strength as the response variable. (a) Interaction between press temperature and compression ratio; (b) interaction between pre-steaming time and compression ratio. A significant crossover interaction between the pre-steaming time and compression ratio factors is evident on the response surface (Figure 19b). Since this interaction is significant, a low level of pre-steaming enhances the effect of a higher compression ratio on MOR performance. Inoue et al. [24] reported a similar detrimental effect on the mechanical properties due to high pre-steaming conditions, specifically when the steam temperature exceeded 180 °C for 10 min. The authors attributed the observed reduction in mechanical performance to the degradation of the microfibril framework and the viscous flow of matrix components, likely resulting from the breakdown of cross-linked structures within the wood. The regressions model for the compression strength including the statistically significant main factors’ effect and two-way interactions is as follows: 𝑦𝜎𝑐=59.2−2.5𝑥1+7.4𝑥2+5.5𝑥3−1.2𝑥1𝑥2+2.0𝑥2𝑥3, 𝑅2=0.62 where xi is the coded variable of the main factor effects (−1 ≤ xi ≤ +1); xixj are the coded variables of the factor’s two-way interactions; x1 represents the pre-steaming time (factor A), x2 represents the compression ratio (factor B), x3 represents the press temperature (factor C), x1x2 represents the AB factor interaction, and x2x3 represents the BC factor interaction. The model approximates the dependent variable (compression strength) at yσC = 77.8 MPa at the factors’ optimum level, which is comparable with the average of the measured data at experimental setup #15. The higher coefficient of determination indicates a better fit of the model to the observed data. 3.5. Hardness Figure 20 shows that the average hardness values of all densified samples exceeded the control value of 1384 N measured on the tangential surface following the ASTM D 143 standard (Section 13) [15]. However, for setups #1, #2, #5, #6, and #9, the Tukey HSD test did not reveal significant differences compared with the control value. Forests 16 00323 g020 Figure 20. Average hardness of the 16 experimental setups and control samples. Among the 16 experiments, five setups (#7, #8, #11, #15, and #16) yielded significantly higher hardness values, with setup #11 achieving the highest average hardness of 3519 N. When a low steaming time (30 min), high compression ratio (50%), low pressing temperature (110 °C), and high pressing time (30 min) were used, the hardness increased by 154% compared with that of the control samples. This increase aligns well with the findings of Peliet and Yorulmaz [25], who reported that 40% densification of poplar wood led to a 138% increase in tangential hardness. Budakçı et al. [26] reported a 147% increase in hardness for black poplar when a pressing temperature of 140 °C was applied alongside a 50% compression ratio. When the maximum value of 3519 N is compared with those of solid red oak or white oak, which are widely used wood species for flooring, it can be concluded that this value is considerably lower than the average values of 6300 N or 6600 N published by Uzcategui et al. [27]. The results of the Pareto analysis (Figure 21) support the fact that the compression ratio has an exceptionally strong effect on surface hardness. Forests 16 00323 g021 Figure 21. Pareto chart of the effects on the hardness. The SEE value of the compression ratio is 2.6 times greater than that of the press temperature, which is the second most significant factor. The remarkable effect of the compression ratio on hardness has already been confirmed by numerous previous studies [2]. The third significant factor is the pressing time, with an SEE value of 2.68. Laine et al. [18] reported the beneficial effect of a longer pressing time at higher temperatures on surface hardness. The pre-steaming time did not significantly affect the hardness properties; however, setting this factor to a low level (30 min) was beneficial. The strongest interaction was observed between the compression ratio and the pressing temperature. Owing to the spreading interaction type, setting both factors to high levels significantly contributed to the increase in hardness (Figure 22). Budakçı et al. [26] reported the same phenomenon during the densification of black poplar and linden species when different pressing temperatures and compression ratios were combined. Forests 16 00323 g022 Figure 22. Response surface plots of the hardness as response variable. The regressions model for the hardness, including the statistically significant main factors’ effect and two-way interactions, is as follows: 𝑦𝑀𝑂𝑅=2492.2−147.3𝑥1+627.1𝑥2+321.9𝑥3+229.1𝑥2𝑥3 𝑅2=0.55 where x1 represents the pre-steaming time (factor A), x2 represents the compression ratio (factor B), x3 represents the press temperature (factor C), and x2x3 represents the BC factor interaction. The model approximates the dependent variable (hardness) at 3917.6 MPa value at the factors’ optimum level, which is comparable with the average of the measured data at experimental setup #15. The modest coefficient of determination indicates a moderate fit of the model to the observed data. 4. Conclusions The comprehensive factorial analysis demonstrates that the compression ratio is the most influential factor affecting the mechanical and physical properties of yellow poplar (Liriodendron tulipifera) densified wood, significantly enhancing compression strength, bending strength, and hardness. However, a higher compression ratio (50%) also resulted in a significant disadvantage, leading to greater spring-back compared to the 20% compression ratio. Pressing temperature is another critical factor, with higher levels (150 °C) boosting strength, hardness, and spring-back performance. The analysis revealed the negative effect of longer pre-steaming treatment on the examined mechanical properties. Pressing time, though less significant as an independent factor, positively interacts with high compression ratios and pressing temperatures to improve performance. Notable interactions between factors were observed. High compression ratios combined with elevated pressing temperatures significantly improved strength and hardness. The densification process applied in this study increased compression strength by 117%, bending strength by 60%, and hardness by 154% compared to control samples. Lower coefficients of determination values of the regression models, fitted on the observed data, indicated that the high variability of the individual measurements mask the involved factors’ main effect. The enhanced properties of densified yellow poplar (Liriodendron tulipifera) suggest its potential for high-performance structural applications, reducing reliance on exotic hardwood species. However, further research should evaluate the long-term performance of densified yellow poplar (Liriodendron tulipifera) under diverse environmental conditions to assess its durability and suitability for outdoor applications. These findings highlight the need for precise optimization of process parameters to balance mechanical performance and dimensional stability, particularly at higher compression ratios where variability challenges may arise.

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Abstract Logging operations of Liriodendron tulipifera L., as timber trees, and fallen leaves in autumn from ornamental trees produce a large amount of leaf waste. In this study, the allelopathy of L. tulipifera fresh and fallen leaves was investigated for the development of potential applications of leaf waste. The extracts of fresh and fallen leaves of L. tulipifera showed growth inhibitory activity against weed species, Vulpia myuros (L.) C.C.Gmel., Echinochloa crus-galli (L.) P.Beauv., and Lolium multiflorum Lam., under laboratory conditions. The powder of L. tulipifera fresh and fallen leaves also inhibited the germination of E. crus-galli under greenhouse conditions. A potent allelochemical was isolated from fresh and fallen leaf extracts through a bioassay-guided separation process, and was identified as lipiferolide. Lipiferolide inhibited the growth of L. multiflorum and Lepidum sativum in a concentration-dependent manner. This investigation suggests that the leaf waste of L. tulipifera from logging operations and fallen leaves is potentially useful for the purpose of weed control, such as through the use of soil additive materials from leaves or the creation of foliar spray from leaf extracts. The development of weed control materials using L. tulipifera leaf waste may be a means with which to minimize waste, reducing environmental impacts and economic concerns. Keywords: allelochemical; industrial waste; lipiferolide; ornamental tree; soil mixture; sustainability; timber processing; waste management

1. Introduction Liriodendron tulipifera L., belonging to the Magnoliaceae family, is a hardwood tree and grows to 25–40 m in height as well as to 1–2 m in trunk diameter. The alternate simple leaves are palmately lobed and grow up to 10–15 cm long and wide [1,2]. The species is native to eastern North America and was introduced to many other countries under temperate climate conditions in Europe and East Asia [3,4,5]. L. tulipifera grows along residential streets as shade trees and in parks as well as gardens as ornamental trees [6,7,8]. L. tulipifera is also valuable as a nectar source for honey production. A 20-year-old tree of L. tulipifera was recorded to produce 3.6 kg of nectar in one season, which is equivalent to 1.8 kg of honey [6]. Since L. tulipifera grows rapidly and has tall and straight trunks, its timber is commercially valuable [2,3,9]. The average dry weight of the timber is 455 kg per m3 (hardness, 2400 N) [9], and the wood of L. tulipifera is used in a wide range of products, such as furniture, interior finishing, and plywood [10,11,12]. However, logging operations of L. tulipifera from tree plantations create a great amount of industrial waste, such as leaves, branches, bark, and stumps. It was recorded that 80% of total tree biomass went to waste during logging operations [13]. Minimizing waste may contribute to the sustainability of timber production systems and reducing the environmental impact [14,15]. The development of possible applications for the waste from timber processing and the minimization of the environmental impact are two tasks to be completed for future timber production systems. Several options of the usage of L. tulipifera waste from logging operations have been investigated, such as charcoal and bio-oil from the wood waste [13,16], solid fuel from the sawdust [17], and bioethanol from the branches and wood [18,19]. In addition, L. tulipifera is a deciduous tree species, and its fallen leaves accumulate on the streets and grounds in parks and gardens in the autumn, where they are grown as shade and ornamental trees. These fallen leaves need to be organized at a high expense [20]. However, the utilization methods of leaf waste from logging operations and fallen leaves from residential areas have not yet been investigated. The bark of L. tulipifera has been used by Native Americans as an antipyretic for the treatment of malaria [21,22,23,24]. Pharmaceutical investigations have shown that L. tulipifera bark contains several secondary metabolites, such as alkaloids and lignans [25]. Its wood, roots, and leaves have also been reported to contain many secondary metabolites, such as alkaloids, terpens, flavonones, lignans, and steroids, and some of them have shown pharmacological activity [23,24,26,27,28,29,30,31]. Although the allelopathic activity of these identified compounds in L. tulipifera has not yet been investigated, some of these compounds may work as allelopathic agents. Allelopathy is the chemical interaction between donor and receiver plants, and donor plants can release certain secondary metabolites, defined as allelochemicals, to their neighboring environments [32,33,34,35]. Allelochemicals affect the germination, growth, development, and regeneration of plant species nearby, including weed plant species, and most allelochemicals show inhibitory effects [36,37,38,39]. Therefore, allelopathic plants and their tissues, which contain allelochemicals, can potentially be applied for weed control purposes. Some allelopathic plants have shown excellent weed control ability when these plants were incorporated into the soil as soil additives and when their extracts were applied to weeds as a foliar spray [40,41,42,43]. Weed infestation causes significant problems in agricultural production [44,45,46]. However, there is no available information on the allelopathy of L. tulipifera leaves in the literature. The objective of this research was to develop a possible application of L. tulipifera leaf waste from logging operations from their use as timber trees and from fallen leaves accumulated on the streets and on the grounds of parks and gardens in autumn (Figure 1). Therefore, we investigated the allelopathic activity of L. tulipifera fresh and fallen leaves against the weed species V. myuros, E. crus-galli, and L. multiflorum in laboratory conditions, and characterized the allelochemicals from the leaves. The fresh leaves were used as substitutes for leaf waste from the logging operations of L. tulipifera. V. myuros, E. crus-galli, and L. multiflorum are common weed species in temperate climate conditions in North America, Europe, and Asia [45,46], and E. crus-galli is also a common weed species in tropical and temperate areas worldwide [47]. We also determined the allelopathic activity of the fresh and fallen leaf powder applied in soil under greenhouse conditions. Applsci 14 08437 g001 Figure 1. L. tulipifera fresh and fallen leaves.
2. Materials and Methods 2.1. Plant Materials The fallen and fresh leaves of L. tulipifera were collected on the campus of Kagawa University, Faculty of Agriculture, on November 2020 and July 2021, respectively. The fresh leaves are substitutes for the waste from logging operations of L. tulipifera trees. The fallen and fresh leaves were dried under shade conditions. Weed species—Vulpia myuros (L.) C.C.Gmel., Echinochloa crus-galli (L.) P.Beauv., and Lolium multiflorum Lam.—were used as bioassay test plant species for the determination of the allelopathic potential of L. tulipifera fallen and fresh leaves. A dicotyledonous plant species, Lepidum sativum L., was used as a bioassay plant through a bioassay-guided separation process of allelochemicals, because the species is a crop plant and shows a high as well as stable germination rate. 2.2. Determination of the Allelopathic Activity of L. tulipifera Fresh and Fallen Leaves under Laboratory Conditions The fresh leaves of L. tulipifera were extracted, and the allelopathic activity of the extracts was determined by the procedure described in the published papers [48]. Test plant seeds were germinated in the dark at 25 °C for 48 h, and 10 germinated seeds of V. myuros, E. crus-galli, and L. multiflorum were incubated in Petri dishes with the extracts in darkness at 25 °C. After 48 h incubation, the length of the coleoptiles and roots of these weed species was determined. Petri dishes contained the extracts obtained from 1, 3, 10, 30, or 100 mg L. tulipifera leaves per 1 mL of the assay solution. The experiment was independently repeated four times with 10 test plants for each determination. The fallen leaves of L. tulipifera were extracted, and the allelopathic activity of the extracts was determined with the same procedure as for the fresh leaves. The allelopathic activity of the fresh and fallen leaf extracts of L. tulipifera was also evaluated using L. sativum. The L. sativum bioassay was carried out with the same procedure described for the weed species except the pre-germination process. L. sativum seeds (not germinated) were incubated with the leaf extracts of L. tulipifera. 2.3. Allelopathic Activity of L. tulipifera Fresh and Fallen Leaves under Greenhouse Conditions The greenhouse experiments were followed by the procedure described in published papers [48]. The powder of the fresh and fallen leaves of L. tulipifera was separately applied onto the soil surface in the pots at powder dosages of 0 (control), 0.21, 0.7, 2.1, 7, and 21 g per pot. After these pots were kept in a greenhouse for 9 days, 100 seeds of E. crus-galli were sown on the soil in the pots. The seeds were germinated under the conditions of natural daylight (average daylight of 12.5 h), temperature (average of 14.7 °C), and watering (500 mL, every two days). After 14 days of incubation, the germinated seeds of E. crus-galli were counted. Control seeds were germinated with exactly the same process except for the L. tulipifera leaf powder. The experiment was independently repeated four times with 100 seeds of E. crus-galli for each determination. 2.4. Isolation of an Allelochemical from L. tulipifera Fresh and Fallen Leaves The fresh L. tulipifera leaves (100 g dry weight) were extracted, and the extracts were purified through a bioassay-guided separation process based on the procedure described in published papers [48]. The ethyl acetate fraction was separated using silica gel chromatography, Sephadex LH-20 chromatography, reverse-phase ODS cartridges (Figure 2, Figures S1–S6). The allelopathic activity of all fractions was determined using a L. sativum bioassay. An active compound, 1, was finally isolated using reverse-phase HPLC in a fraction eluted between 168 and 172 min. The chemical structure of compound 1 was determined by the analysis of spectrum data of 1H-NMR (400 MHz, CDCl3), HRESI-MS, and optical rotation. Applsci 14 08437 g002 Figure 2. Separation process of compound 1 from L. tulipifera leaf extracts. The fallen leaves of L. tulipifera were extracted and purified as described for the fresh leaves, and the same active compound was detected in a fraction eluted between 168 and 172 min of the HPLC. The active compound isolated from the fallen leaves is identical with compound 1 based on its chromatographic property. 2.5. Allelopathic Activity of the Isolated Compound The allelopathic activity of compound 1 isolated from L. tulipifera leaves was determined using a bioassay of L. multiflorum and L. sativum. The concentrations of lipiferolide in the bioassay were 0.01, 0.03, 0.1, 0.3, and 1 mM. The bioassay was independently repeated four times with 10 germinated seeds (L. multiflorum) and 10 seeds (L. sativum) for each determination. 2.6. Statistical Analysis All bioassay treatments were set in a randomized block design, and repeated four times. The means ± SE (standard error) of the collected data were calculated. Significant differences were evaluated by an ANOVA one-way analysis (SPSS, version 16.0) and a post hoc analysis with Tukey’s HSD test at a p < 0.05 level. The values of the concentration causing 50% growth inhibition (IC50) of test plant species in the respective bioassay were obtained using GraphPad Prism 6.0.
3. Results 3.1. Allelopathic Activity of L. tulipifera Fresh and Fallen Leaves under Laboratory Conditions The fresh leaf extracts of L. tulipifera significantly suppressed the growth of coleoptiles of V. myuros, E. crus-galli, and L. multiflorum at concentrations ≥ 10 mg equivalent leaf extracts, and suppressed the root growth of these weed species at concentrations ≥ 3 mg equivalent extracts per mL (Figure 3). Applsci 14 08437 g003 Figure 3. Effects of L. tulipifera fresh leaf extracts against the coleoptile and root growth of V. myuros, E. crus-galli, and L. multiflorum under laboratory conditions. The concentrations on the X-axis indicate the substances obtained from 1, 3, 10, 30, and 100 mg of L. tulipifera leaves in 1 mL of the assay solution. The means ± SE were calculated from four independent experiments with 10 seedlings for each determination. Different letters on the bars in the same panels indicate significant differences at a p < 0.05 level. The fallen leaf extracts of L. tulipifera also suppressed the growth of V. myuros, E. crus-galli, and L. multiflorum coleoptiles at concentrations ≥ 30 mg equivalent leaf extracts per mL, and suppressed the growth of V. myuros, E. crus-galli, and L. multiflorum roots at concentrations ≥ 10 mg equivalent leaf extracts per mL (Figure 4). The IC50 value of the fresh and fallen leaf extracts for the coleoptile and root growth was shown in Table 1. Applsci 14 08437 g004 Figure 4. Effects of the L. tulipifera fallen leaf extracts against the coleoptile and root growth of V. myuros, E. crus-galli, and L. multiflorum under laboratory conditions. Other conditions were the same as those described in Figure 3. Table 1. IC50 values of the fresh and fallen leaf extracts of L. tulipifera on the coleoptile and root growth of bioassay test plant species. The values were determined by the regression equation of the concentration response as described in the text.

The fresh and fallen leaf extracts of L. tulipifera also suppressed the hypocotyl and root growth of L. sativum at concentrations ≥ 10 mg leaf equivalent extracts per mL at a p < 0.05 level (Figure 5). The IC50 value of the fresh leaf extracts for the L. sativum hypocotyl and root growth was 4.7 mg and 5.1 mg equivalent extracts per mL, respectively, and that of the fallen leaf extracts for the L. sativum hypocotyl and root growth was 10.2 mg and 18.8 mg equivalent extracts per mL, respectively. Applsci 14 08437 g005 Figure 5. Effects of the fresh (A) and fallen (B) L. tulipifera leaf extracts against the growth of L. sativum hypocotyls and roots. Other conditions were the same as those described in Figure 3. 3.2. Allelopathic Activity of L. tulipifera Fresh and Fallen Leaves under Greenhouse Conditions The fresh and fallen leaf powder of L. tulipifera suppressed E. crus-galli germination under greenhouse conditions at dosages ≥ 0.7 g and 2.1 g powder per pot at a p < 0.05 level, respectively (Figure 6). The suppression of germination was increased with an increase in the powder dosage. The powder dosages at 0.21, 0.7, 2.1, 7, and 21 g per pot (area: 0.07 m2) corresponded to nearly 0.03, 0.1, 0.3, 1, and 3 tons of the powder per hectare (10,000 m2), respectively. The IC50 values of the leaf powder for E. crus-galli germination was 1.7 g and 5.2 g per pot for fresh and fallen leaves, respectively. E. crus-galli is one of the problematic weeds in agricultural production [47,49]. Applsci 14 08437 g006 Figure 6. Effects of the fresh (A) and fallen (B) leaf powder of L. tulipifera against the germination of E. crus-galli under greenhouse conditions. The means ± SE were calculated from four independent experiments with 100 seeds for each determination. Different letters on the bars in the same panels indicate significant differences at a p < 0.05 level. 3.3. Isolation and Identification of Allelochemicals in L. tulipifera Fresh and Fallen Leaves The fresh leaf extract of L. tulipifera was purified through the bioassay-guided separation process (Figure 2); 12 and 4 mg of compound 1 were isolated from 100 g dry weight of the fresh and fallen leaves, respectively. The molecular formula of compound 1 was determined as C17H22O5 via HRESI-MS analysis. The 1H-NMR spectrum showed δH 6.39 (d, J = 3.6 Hz, 1 H, H13), 5.71 (d, J = 3.1 Hz, 1 H, H13), 5.69 (brd, J = 4.9 Hz, 1 H, H8), 5.29 (dd, J = 12.6, 2.7 Hz, 1 H, H1), 4.40 (t, J = 8.3 Hz, 1 H, H6), 3.13 (dt, J = 7.2, 3.4 Hz, 1 H, H7), 2.82 (d, J = 8.8 Hz, 1 H, H5), 2.75 (dd, J = 14.4, 4.9 Hz, 1 H, H9), 2.47 (m, 1 H, H2), 2.36 (brd, J = 14.4 Hz, 1 H, H9), 2.24 (m, 1 H, H2), 2.19 (ddd, J = 13.0, 6.5, 1.8 Hz, 1 H, H3), 2.06 (s, 3 H, H16), 1.76 (s, 3 H, H15), 1.37 (s, 3 H, H14), 1.29 (m, 1 H, H3). The optical rotation was [α]D27 −104 (c 0.1, CH3OH). Compound 1 was determined to be lipiferolide (sesquiterpene lactone) (Figure 7) based on the comparison of its spectrum data with the spectrum data in publication [50]. Applsci 14 08437 g007 Figure 7. Chemical structure of lipiferolide. 3.4. Allelopathic Activity of the Isolated Compound Lipiferolide showed significant growth inhibition at a p < 0.05 level on the L. sativum hypocotyls and roots at concentrations ≥ 0.1 mM, as well as on the L. multiflorum coleoptiles and roots at concentrations ≥ 0.3 mM (Figure 8). The IC50 value of lipiferolide for the L. sativum hypocotyl and root growth was 0.51 mM and 0.4 mM, respectively, and its IC50 value for the L. multiflorum coleoptile and root growth was 0.61 mM and 0.47 mm, respectively. Applsci 14 08437 g008 Figure 8. Effects of lipiferolide on the L. sativum hypocotyl and root growth, as well as on the L. multiflorum coleoptile and root growth. The means ± SE were calculated from four independent experiments with 10 seeds (L. sativum) and 10 germinated seeds (L. multiflorum) for each determination. Different letters on the bars in the same panels indicate significant differences at a p < 0.05 level. 4. Discussion 4.1. Allelopathic Activity of the Fresh and Fallen Leaves of L. tulipifera L. tulipifera is a popular timber species, and logging operations of the trees produce a large amount of leaf waste [1,2,3,6,7,8,9]. L. tulipifera is also grown in residential areas as shade and ornamental trees, and the fallen leaves accumulate on the streets and ground in the autumn. These fallen leaves need to be organized at a large expense [20]. However, there is no information available on the allelopathic activity and allelochemicals in the leaves of L. tulipifera on the literature. Therefore, we have examined the allelopathic activity of L. tulipifera fresh and fallen leaves under laboratory and greenhouse conditions. The fresh leaves were used as substitutes for leaf waste from the logging operations of L. tulipifera. The fresh leaf extracts showed significant growth inhibition on the roots and coleoptiles of weed plant species—V. myuros, E. crus-galli, and L. multiflorum—under laboratory conditions (Figure 3). The fallen leaf extracts also showed significant growth inhibition on these weed species (Figure 4). However, the sensitivity of these weed species differed to the fresh and fallen leaf extracts, and differed in the organs of test plant species (coleoptiles and roots). Comparing their IC50 values, the sensitivity of the E. crus-galli roots to the fresh leaf extracts was the highest among the roots and coleoptiles of these weed species, and that of the L. multiflorum coleoptiles to the fallen leaf extracts was the lowest (Table 1). The IC50 values of the fallen leaf extracts were 2- and 2.8-fold, 4.3- and 7.2-fold, and 6.5- and 6.6-fold greater than those of the fresh leaf extracts for the coleoptiles and roots of V. myuros, E. crus-galli, and L. multiflorum, respectively, which indicates that the allelopathic activity of the fresh leaves is greater than that of the fallen leaves. These results suggest that the L. tulipifera fresh and fallen leaves may contain extractable allelochemicals, and the fresh leaves may contain more allelochemicals than the fallen leaves. The fresh and fallen leaf powder of L. tulipifera significantly suppressed the germination of E. crus-galli under greenhouse conditions (Figure 6). The IC50 value of the fallen leaf powder was 3.1-fold greater than that of the fresh leaf powder, which consists of the IC50 values of the fresh and fallen leaf extracts (Table 1). As described in Materials and Methods (Section 2.3.), E. crus-galli seeds were sown 9 days after the leaf powder was applied onto the soil. Therefore, allelochemicals in the L. tulipifera leaf powder may be released into soils within 9 days, and may cause the suppression of the germination (Figure 6). In addition, the amount of released allelochemicals from the fresh leaves may be greater than that from the fallen leaves. As described in the Introduction, when certain plant parts of some allelopathic plant species were incorporated into soil, these plant parts suppressed the germination and growth of several weed species in greenhouse and field conditions due to their allelochemicals [40,41,42,43,44]. When the soaking water and extracts of some allelopathic plant leaves were applied to several weed species as a foliar spray and/or irrigation water, the germination and growth of several weed species were restricted [40,41,42,43,44,51,52,53,54,55,56,57]. The fresh and fallen leaves of L. tulipifera may contain certain allelochemicals (Figure 3, Figure 4 and Figure 5), and some of these allelochemicals in the leaves may be released into soils and cause the suppression of the germination of weed species (Figure 6). Therefore, the fresh and fallen leaves of L. tulipifera are potentially useful as soil additive materials to control weeds in a variety of agricultural practices. The leaf extracts may also be applied as a foliar spray to control weeds. However, the weed control activity of the leaves in a soil additive material and that of the leaf extracts as a foliar spray under large-field condition should be evaluated. 4.2. An Allelochemical in L. tulipifera Leaves The fresh and fallen leaf extracts of L. tulipifera were purified through the bioassay-guided separation process using silica gel chromatography, Sephadex LH-20 chromatography, an ODS cartridge, and reverse-phase HPLC (Figure 2). During the separation process, the allelopathic activity of all separated fractions was determined, and the most active fraction was applied to the next separation process. L. sativum was selected as a bioassay plant species for the isolation process because of its stable germination rate and easy handling [58]. The germination of L. sativum was high (about 90% germination rate at 48 h after sowing) compared with the weed species—V. myuros, E. crus-galli, and L. multiflorum (20–40% germination rate). The sensitivity of the L. sativum hypocotyls and roots to the L. tulipifera fresh and fallen leaf extracts was not too high and not too low in comparison with the IC50 values of these weed species (Figure 3, Figure 4 and Figure 5 and Table 1). An active compound, 1, was isolated from both fresh and fallen leaf extracts and characterized by the spectrum data as lipiferolide (Figure 7). Lipiferolide suppressed the growth of L. sativum and L. multiflorum in a concentration-dependent manner (Figure 8). Lipiferolide was first isolated from L. tulipifera leaves [59,60], and has been isolated from other Magnoliaceae species, Liriodendron chinensis (Hemsl.) Sarg., Magnolia sirindhorniae, Michelia yunnanensis Noot & Chalermglin, and Michelia fuscata (Lour.) DC. [61,62,63,64], and the Asteraceae species Vicoa pentanema Aitch. & Hemsl. [65]. Lipiferolide showed cytotoxic activity against human cancer cell lines, such as KB cells and Hepa lclc7 cells [59,66], and antiplasmodial activity [23]. However, it is the first report on the biological activity and allelopathic activity of lipiferolide against plant species, including weed species. The mode of action of lipiferolide on allelopathic activity and its concentration in soil should be evaluated in the furfure. Lipiferolide is only commercially available for laboratories but not for practical use. Lipiferolide in the leaves of L. tulipifera may be involved in the allelopathic activity of the fresh and fallen leaf extracts and the leaf powder (Figure 3, Figure 4 and Figure 6). The fresh leaves may contain greater amounts of lipiferolide than fallen leaves because 12 mg and 4 mg of lipiferolide were isolated from the fresh and fallen leaves, respectively. One of the reasons for the lower amount of lipiferolide in the fallen leaves may be the metabolism of the compound during the defoliation and/or degradation process of the leaves [67,68,69,70]. The concentrations of lipiferolide in the fresh and fallen leaves are consistent with the allelopathic activity of the extracts and powder obtained from the fresh and fallen leaves (Figure 3, Figure 4, Figure 5 and Figure 6). Therefore, lipiferolide in the fresh and fallen leaf powder of L. tulipifera may be liberated into soil at the same proportion and suppress germination. 5. Conclusions The fresh and fallen leaf extracts and leaf powder of L. tulipifera showed allelopathic activity against weed species—V. myuros, E. crus-galli, and/or L. multiflorum—under laboratory and greenhouse conditions. Lipiferolide was isolated from the fresh and fallen leaves of L. tulipifera as an allelochemical. Lipiferolide inhibited the growth of L. sativum and L. multiflorum in a concentration-dependent manner. Therefore, both fresh and fallen leaf extracts, as well as the leaves themselves, can be applied as a foliar spray and soil mixture, respectively, to suppress the germination and growth of certain weed species. Therefore, the leaves of L. tulipifera are potentially useful for the purpose of weed control. Logging operations of timber trees and the fallen leaves in autumn of ornamental plants produce a large amount of leaf waste. The development of weed control materials using L. tulipifera leaf waste may be one solution to reduce environmental impacts and economic concerns.

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The DOF (DNA-binding with one finger) transcription factors are exclusive to plants and play crucial roles in plant growth, development, and environmental adaptation. Although extensive research has been conducted on the Dof gene family in Arabidopsis, maize, and Solanum, investigations concerning the role of this gene family in Liriodendron remain unreported, leaving its biological function largely unknown. In this study, we performed a comprehensive genome-wide identification of the Dof gene family based on the Liriodendron genome, resulting in the discovery of a total of 17 LcDof gene members. Based on the results of phylogenetic analysis, the 17 LcDof proteins were classified into eight subfamilies. The motif analysis revealed the diverse nature of motifs within the D1 subfamily, which includes a distinct type of Dof transcription factor known as CDF (Cycling Dof Factor). We further characterized the chromosomal distribution, gene structure, conserved protein motifs, and cis-elements in the promoter regions. Additionally, utilizing transcriptome data from Liriodendron hybrids and conducting RT-qPCR experiments, we investigated the expression patterns of LhDofs under various abiotic stresses such as drought, cold, and heat stress. Notably, we found that several LhDofs, particularly LhDof4 and LhDof6, were significantly upregulated in response to abiotic stress. Furthermore, we cloned LhDof4 and LhDof6 genes and found that its encoding protein was mainly located in the nucleus by transient transformation in Liriodendron hybrids protoplast. Subsequently, we used LhDof6-overexpressing Liriodendron hybrid seedlings. We found that overexpression of LhDof6 enhanced the cold tolerance of the plants, increasing their survival rate at −20 °C. This result was further validated by changes in physiological indicators. Keywords: DNA-binding with one finger; Liriodendron; abiotic stress

1. Introduction The presence of abiotic stresses, such as high salinity, drought, extreme temperatures, and poor soil fertility, poses significant environmental challenges that critically impede plant growth, development, and overall productivity [1]. Throughout the process of evolution, plants have demonstrated their ability to gradually colonize a wide range of terrestrial environments, including those characterized by harsh and extreme conditions. This remarkable adaptability has been facilitated by the development of sophisticated molecular and physiological mechanisms that enable plants to regulate their growth in response to resource availability and prevailing environmental conditions. The aforementioned adaptations have played a pivotal role in enabling plants to flourish in various ecosystems, ranging from arid deserts to saline marshes, thereby showcasing their remarkable resilience and adaptability in surmounting environmental constraints [1,2,3,4,5]. Numerous transcription factors (TFs) involved in the regulation of gene expression and signaling pathways related to abiotic stress have been identified, encompassing a diverse range of members from large gene families such as bHLH, HD-ZIP, WRKY, MYB, bZIP, DOF, and NAC [6,7,8,9,10]. The Plant-specific DNA Binding with One Finger (DOF) proteins are a group of transcription factors (TFs) characterized by a conserved 50-amino-acid DNA-binding domain, typically located in their N-terminal region and connected to a basic region [11]. The conserved DOF domain is a distinct zinc finger domain, characterized by a C2–C2 finger structure. It specifically binds to cis-regulatory DNA elements featuring the core 5′-T/AAAG-3′ motif, which is found in the promoter regions of target genes [12,13]. Recent studies have revealed that, despite its initial identification as a DNA-binding domain, the DOF domain may possess a plethora of functions, including nuclear localization, interaction with other transcription factors and intercellular trafficking [14,15]. Previous studies have corroborated its functional role in plant growth and development, including flowering control [16], maturation, seed development [17], and germination [18,19]. In particular, mutant dag1 (which encodes a Dof transcription factor in Arabidopsis) seeds are induced to germinate by significantly red light fluence rates [20]; the COG1 gene (which encodes a Dof protein in Arabidopsis) functions as a negative regulator in phytochrome signaling pathways [21]. Additionally, compelling evidence suggests that CYCLING DOF FACTORS (CDFs), a class of Dof-type transcriptional repressors, have been experimentally proven to directly suppress the expression of CONSTANS (CO). CDFs possess the ability to inhibit the expression of photoperiodic genes, thereby influencing the perception of day length and ultimately impacting the floral transition in Arabidopsis [22]. More significantly, Dof transcription factors play a crucial role in plant phytohormone and stress responses. For instance, TDDF1, which encodes a Dof protein in tomato, enhances tolerance to drought, salt, various hormonal stresses, as well as resistance to late blight [23]. The salt and osmotic stress tolerance is enhanced by ThZFP1 and ThDof1.4 through the elevation of proline levels and improvement in ROS scavenging capability [24]. Therefore, the Dof gene family plays an essential role in the life cycle of plants. The Liriodendron hybrids display significant heterosis, encompassing not only distinct foliar morphology and exotic floral characteristics but also notable adaptive capabilities and growth advantages. Historically, the Liriodendron hybrids have shown sensitivity to low-temperature stress, which has posed a considerable challenge. The study and functional validation of the Dof gene family may provide a potential avenue for identifying candidate genes that can be utilized for genetic improvement and the development of cold tolerance in Liriodendron hybrids. Furthermore, proposing further application of genetic engineering techniques for developing novel cold-resistant Liriodendron hybrids aims to extend these new varieties’ reach to facilitate ecosystem services across a broader geographical area.
2. Results 2.1. Identification and Protein Sequence Characterization of LcDofs The Dof gene has been previously confirmed to be broadly involved in plant growth and development. Recent reports have also identified its significant role in plant responses to abiotic stress. The Dof gene in Liriodendron chinense (L. chinense) has been the subject of initial exploration and analysis. The L. chinense genome contains a total of 17 Dof genes, accounting for 0.048% of the overall gene count in the genome (Table 1). Table 1. LcDof genes and their related information. (Len: protein length; MW: molecular weight; PI: isoelectric point; AI: aliphatic index; II: Instability index).

The Dof transcription factor family in L. chinense is relatively small compared to the majority of species. Based on their chromosomal locations, these genes have been designated as LhDof1 to LhDof17 (Table 1). To investigate the genetic diversity within this family, we conducted a comprehensive analysis of the fundamental physicochemical characteristics of these 17 Dof proteins, encompassing protein sequence length, molecular weight (MW), isoelectric point (pI), and other pertinent properties. The analysis of the physicochemical properties of the LcDof gene family revealed that the Dof proteins exhibit a length range of 160 to 635 amino acids, with molecular weights varying from 17.01 to 71.60 kDa. The isoelectric point (pI) of LcDof7 (Lchi21078) was observed to be the lowest at 5.70, while the highest pI value of 9.56 was noted for LcDof5 (Lchi18955), with an average pI of 7.76. The analysis indicates that the majority of Dof proteins exhibit a weakly basic nature. Most LcDof proteins are characterized by high levels of instability, with the highest instability index score reaching 85.81. Among this group, only LcDof11 (Lchi08966) is considered stable, as it exhibits an instability index value below 40. The LcDof proteins are susceptible to denaturation or degradation, leading to potential alterations in their physicochemical properties and biological functions. This implies that LcDof proteins exhibit a high sensitivity towards changes in the external environment. The physicochemical properties of Dof proteins in L. chinense display variations, suggesting diverse regulatory roles in plant growth and development under different conditions. Therefore, it is crucial to conduct a comprehensive investigation into their classification and phylogenetic traits. 2.2. LcDofs Contain More Abundant Conserved Motifs and a More Homogeneous Gene Structure A multiple sequence alignment of 17 LcDof amino acid sequences from L. chinense was performed using the ClustalX software (V2.1). The conserved domains were then analyzed. It was found that the N-terminal Dof domain of the L. chinense Dof protein contains a typical C2–C2 zinc finger protein structure, comprising 52 amino acid residues (Figure 1). The LcDof protein domain remains intact and exhibits a remarkable level of uniformity, comprising a solitary zinc finger protein composed of four cysteines. This observation signifies the highly conserved and complete nature of the Dof domain in L. chinense. The LcDof gene family is classified into 6 subgroups based on multiple sequence alignment and phylogenetic analysis, with each subgroup exhibiting nearly identical motif structure and distribution of LcDof proteins (Figure 2). Moreover, genes within the same subgroup share analogous intron-exon structures and gene lengths (Figure 2). All LcDof proteins contain Motif1 (Figure 2), which is consistent with the findings of previous studies. Motif1 is the conserved Dof motif of LcDof. Our findings indicate that the C-terminal structures of four LcDof proteins (Lchi19181, Lchi21078, Lchi14330, Lchi02891) within a specific subgroup are characterized by the presence of diverse types of motifs. This observation suggests that the LcDof proteins in this subgroup may play a role in a multitude of biological processes. The presence of other motifs, such as Motif 4, 6, and 9, is limited to only a single or a few phylogeographic subgroups. Interestingly, in certain categories, no additional conserved sequence motifs were identified besides the Dof motif, implying that the functions of these sequences may be unpredictable. A comprehensive analysis of these sequences may prove crucial in elucidating the functional diversity of the Dof family. Through gene structure analysis, it was observed that the LcDof genes exhibit a distribution of 1 (2 genes), 2 (11 genes), and 3 (4 genes) exons, respectively, suggesting that the predominant form of LcDof genes consists of two exons. Plants 13 02009 g001 Figure 1. The Dof concerved region in LcDofs, alignment of multiple protein sequences in LcDofs. Plants 13 02009 g002 Figure 2. Analysis of conserved motif elements and gene structures of LcDofs. Figure was made using the MEME program and TBtools. 2.3. Phylogenetic Structure of the LcDof Gene Family In order to elucidate the molecular evolution and phylogenetic relationships of L. chinense Dof proteins, an unrooted phylogenetic tree was constructed containing 17 LcDofs and their homologs in Arabidopsis thaliana, Amborella trichopoda, Zea mays, and Oryza sativa. A multiple sequence alignment of Dof gene family members was performed using MAFFT software (v7.487) (https://mafft.cbrc.jp/alignment/software, accessed on 25 January 2022) with default parameters. The phylogenetic tree was constructed using MAGE 7.0, employing the neighbor-joining method with a bootstrap value of 1000 to analyze the evolution of the Dof gene in L. chinense (Figure 3). The results of multiple sequence alignment and phylogenetic tree clustering indicate that the Dof proteins from Liriodendron chinense, Arabidopsis thaliana, Amborella trichopoda, Zea mays, and Oryza sativa can be classified into eight distinct categories: Class A, B1, B2, C1, C2, C3, D1, and D2 (Figure 3). Additionally, L. chinense and A. trichopoda exhibit a closer phylogenetic relationship. Plants 13 02009 g003 Figure 3. Phylogenetic trees of Dof genes. Different colors represent different subclasses, and the red font represent L. chinense. Numbers on branches indicate percent reliability of bootstrap values based on 1000 replicates. 2.4. Analysis of the Whole-Genome Duplication Events in the LcDof Family In order to gain insight into the chromosomal distribution of the LcDof family, we used Tbtools software along with the genomic localization information of the LcDof family members to display their chromosomal distribution (Figure 4). The distribution of LcDof family members across the chromosomes is not uniform, with Dof genes found only on chromosomes 1, 2, 4, 7, 8, 11, 12, 13, 15, 16, and 18 in L. chinense. Among them, chromosomes 1, 2, 4, 13, 16, and 18 each contain two Dof genes, and the genes are closely arranged on chromosomes 1, 13, and 16. The data indicates that each chromosome contains a maximum of two Dof genes, suggesting that LcDof genes are typically spaced at considerable distances and rarely clustered on the same chromosome. The majority of LcDof genes are located at the chromosomal ends, with fewer near the centromeric regions. The concentrated distribution of LcDof genes at the chromosomal ends in L. chinense may be related to the more open chromatin structure found at these locations, which could facilitate active gene expression. Meanwhile, the chromosomal center (near the centromeres) typically has a more compact chromatin structure, which may restrict gene expression [25,26]. Plants 13 02009 g004 Figure 4. Genome-wide synteny analysis of Dof gene family among L. chinense. Distribution and duplication of the LcDof gene in L. chinense. The circles are chromosomal genes, and the names are displayed outside the circles. We have studied and mapped gene duplication events during the evolutionary process of L. chinense, which may also affect changes in the number and distribution of LcDof genes within the genome (Figure 4). It is known that duplication events occur regionally through a process known as tandem duplication, whereby gene sequences of less than 200 kb are copied in close proximity to the original gene. However, they can also occur over a broader range through segmental duplication, where larger fragments are duplicated. The former is typically attributed to DNA replication errors, whereas the latter may be attributed to polyploidy events resulting from chromosomal rearrangements [27,28]. There are six duplication events within the LcDof genes of L. chinense (Lchi21087–Lchi19181, Lchi05004–Lchi11874, Lchi14330–Lchi02891, Lchi07493–Lchi14379, Lchi08966–Lchi13929), of which only one pair (Lchi13427–Lchi13426) is due to tandem duplication, while the rest are caused by segmental duplication. The analysis of the expansion of the LcDof gene family has revealed that the majority of gene duplication events are the result of segmental duplication, which represents a crucial mechanism for gene expansion in plants. The presence of these duplication events reveals significant expansion strategies within the gene family during the evolutionary process and also indicates the dynamic changes and adaptive evolution of plant genomes. The aforementioned duplication pattern, in conjunction with the whole-genome duplication (WGD) event experienced by L. chinense, offers crucial insights into the expansion and adaptation of the LcDof gene family throughout plant evolution. Following whole-genome duplication events, some genes may be lost due to functional redundancy, while genes with essential functions or those providing adaptive advantages are often retained. 2.5. The Analysis of Cis-Element Regulation of Promoters Revealed That LcDofs Regulated Many Bioactive Processes We used the PlantCARE website to analyze the 5′ upstream promoter regions (2000 bp) of the LcDof genes to predict all cis-acting elements. A variety of cis-acting elements were identified, and the promoter regions of all LcDof genes were found to contain a substantial number of them. We classified these cis-acting elements into four categories: growth and development-related elements, light-responsive elements, abiotic stress-responsive elements, and plant hormone-responsive elements (Figure 5). The promoters of LcDof genes were found to be rich in light-responsive and abiotic stress-responsive elements, which suggests that LcDof genes are primarily involved in biological processes related to light response and abiotic stress response. In summary, the distribution of cis-acting elements indicates that LcDofs are involved in light response, hormone response, stress response, and plant growth and development. Plants 13 02009 g005 Figure 5. Results of LcDofs gene promoter predictor elements from the PlantCare database are depicted. The abscissa axis displays the types of elements. Red squares indicate a higher predicted presence of the element in the LcDofs gene promoter, while gray and white squares signify that the element is either absent in the gene promoter or present in lesser numbers. The presence of MYB and STRE environmental response factors in all LcDof genes indicates that these two cis-acting elements are crucial for LcDof genes to perceive environmental alterations. However, some cis-acting elements are specific to certain LcDofs. For example, only LcDof15 (Lchi13929) in Class D2 contains the DRE element, which suggests that Class D2 LcDofs may have differentiated to include proteins that are responsive to dehydration. The LcDof family members contain LTR functional elements, suggesting that some members may be specifically involved in cold response. While not all members within each class contain LTR elements, at least one member in each class is capable of responding to cold, indicating that the LcDof family exhibits some sensitivity to low temperatures. G-Box, a crucial cis-regulatory element in light response, is abundant in all members of the LcDof family, playing a significant role in mediating light-dependent gene expression. The G-Box is commonly found in the promoters of many light-responsive genes, where it binds to light-activated transcription factors to promote transcriptional activation of these genes in response to light stimuli. Other light-responsive elements appear to be irregularly distributed in the promoters of each member, with LcDof4 (Lchi02891) containing more light-responsive elements than other members, indicating differences in light response capabilities within the family. As LcDofs contain abundant ABRE elements, they may be actively involved in the abscisic acid response of plants. The distribution and quantity of ABRE3a and ABRE4 elements are evenly spread among specific LcDof members. This pattern suggests that these genes likely play a consistent and significant role in plant’s response to ABA signaling. The specific distribution of ABRE3a and ABRE4 elements may ensure the coordinated expression of these genes throughout the plant, effectively regulating the physiological state of the plant to adapt to environmental changes [29,30]. While ABRE elements are widely distributed in certain LcDof members, this does not imply that all members have identical functions in all physiological processes or stress responses. The different ABRE types may act as mediators for the binding of specific transcription factors or co-activators, which in turn result in subtle functional differences that enable plants to adapt their response to abscisic acid in a precise manner [31,32]. The results suggest that these LcDof proteins may also be involved in different abscisic acid-mediated regulatory networks. The majority of the growth and development regulatory elements of LcDofs are concentrated in the meristematic and differentiation functions of tissues, suggesting that these genes play a pivotal role in tissue differentiation and development. The cis-regulatory element analysis reveals that the functional elements in the promoter regions of LcDof genes are both abundant and comprehensive, indicating that they may function independently or simultaneously to regulate growth, development, and abiotic stress response. Accordingly, further investigation is required to elucidate the expression profiles of LcDofs under growth conditions and abiotic stress. 2.6. LcDof Gene Families of Class D1 under Abiotic Stress Has a Strong Reaction To study the expression pattern of Liriodendron hybrid Dof genes under different stress conditions, we analyzed their expression profiles in the transcriptome data of leaves under low temperature, PEG6000-simulated drought, and high-temperature stress conditions (Figure 6). The results indicate that LhDof genes in the D1 class are actively expressed, and both LhDof4 (Lchi02891) and LhDof6 (Lchi14330) exhibit strong responses to cold and drought stress, with similar expression patterns (Figure 6A,B). Under low-temperature treatment, the expression levels of LhDof4 and LhDof6 were continuously upregulated from 0 h (CK) to 1 day, peaking at 12 h, and then downregulated from 1 day to 3 days (Figure 6A). Under PEG6000-simulated drought treatment, the expression levels of LhDof4 and LhDof6 were continuously upregulated from 0 h (CK) to 1 h, reaching their highest expression at 1 h, gradually downregulated from 1 h to 12 h, and then slowly upregulated from 12 h to 3 days, finally maintaining normal expression levels (Figure 6B). Plants 13 02009 g006 Figure 6. Transcriptional expression patterns of Dof genes in L. hybrid under (A) cold, (B) drought, and (C) heat stress are depicted. The LhDofs were subjected to three different stress factors: cold, drought, and heat stress. The designations Heat_0h, Heat_1h, Heat_3h, Heat_6h, Heat_12h, Heat_1d, and Heat_3d represent three biological replicates for each time point (0 h, 1 h, 3 h, 6 h, 12 h, 1 day, 3 days). Transcript abundance levels are represented using the log2 (FPKM + 1) transformation. The values on the right panel of the heatmap indicate the expression level. The expression levels of LhDof4 and LhDof6 were significantly downregulated under high-temperature treatment. However, they exhibited high responsiveness to low-temperature and drought stress, displaying similar expression patterns across all three stress conditions (Figure 6C). The D1 class of the Dof family encompasses a specific category of Dof genes, designated as CDF genes, which typically demonstrate robust responses to abiotic stressors. We have confirmed this in Liriodendron hybrid as well. The CDF transcription factors LhDof4 and LhDof6 from the D1 class may play a positive regulatory role under low-temperature and drought stress. The sharply downregulated expression of these two factors under high temperature stress suggests that LhDof4 and LhDof6 have a negative regulatory role in response to high temperatures. Two of the LcDof genes were not expressed under the two stresses, and the expressions of five LcDof genes were insignificant. In summary, only the class D1 CDF transcription factors exhibited a robust response to low-temperature, drought, and high-temperature stress, indicating that the LcDof gene family has evolved class D1 CDF transcription factors that are specifically responsive to diverse abiotic stresses. This is consistent with recent studies on CDFs. For instance, CDF transcription factors can induce the expression of stress-response genes. The A. thaliana AtCDF3 has been shown to regulate the expression of multiple abiotic stress-response genes in plants that respond to extreme temperatures, drought, and osmotic stress [33]. The expression of two CDF genes was observed to undergo significant alterations in response to drought and elevated temperatures, with notable changes occurring within the first hour. This suggests that the LhCDF genes exhibit a high degree of sensitivity to these two stressors. In summary, the pair of CDF genes (LhDof4 and LhDof6) may co-regulate and respond to different abiotic stresses. 2.7. qRT-PCR Validation of LcCDFs under Low Temperature and Drought Stress To verify the accuracy of the abiotic stress transcriptome data for LcCDFs (LhDof4 and LhDof6), we conducted qRT-PCR experiments on Liriodendron hybrids under cold and drought stress (Figure 7). Plants 13 02009 g007 Figure 7. The expression of LhDof4 and LhDof6 in cold and drought-treated samples was analyzed by qRT-PCR. Vertical bars represent standard deviation, with 18S serving as the internal reference gene, and each sample was repeated three times. * on the bar chart indicates that the difference between the two control groups reached a significant level, that is, p < 0.05, *** means p < 0.001, **** means the difference between the two control groups reached a very significant level, that is, p < 0.0001. Under drought stress, the expression of LhDof4 and LhDof6 initially increases, then decreases, and subsequently increases again. The expression levels of both genes drop to their lowest at 12 h, below the control (CK) level, and then increase at 24 h to levels similar to those observed at 1 h. After 1 h, the expression levels significantly increased, then gradually decreased, and finally increased again to return to normal levels. This pattern is consistent with the transcriptome data results under drought conditions. Under low-temperature stress, the expression of LhDof4 and LhDof6 gradually increased over time, peaked at 12 h, maintained a high level until 1 day, and then gradually decreased, aligning with the transcriptome data results. Under low-temperature stress, the expression levels of LhDof4 and LhDof6 gradually increased over time, peaking at 12 h. They maintained a high level until 24 h, after which they gradually decreased. This pattern aligns with the transcriptome data results. 2.8. LhDof4 and LhDof6 Are Localized to the Cell Nucleus We conducted a subcellular localization prediction analysis for all LcDof genes, including LcDof4 and LcDof6, using an online tool. The results of this analysis are presented in Table 2. Most of the LcDof genes are predicted to localize to the nucleus, which is consistent with their roles as transcription factors. However, LcDof10 and LcDof11 are predicted to localize in the chloroplast, while LcDof13 is localized in the mitochondria. All CDFs are shown to be localized in the nucleus, likely due to the presence of one or more nuclear localization sequences in the N-terminal structure of CDF proteins. The LcDof genes also play regulatory roles in the chloroplasts or mitochondria of plant cells, not just in the nucleus. Table 2. Subcellular localization prediction results for LcDof genes.

We then used the cloned LhDof4 and LhDof6 sequences to construct fusion fluorescent protein vectors, p35S:LhDof4-GFP and p35S:LhDof6-GFP, with the unmodified pJlT166 vector serving as a control. The results showed that in protoplast cells transformed with p35S:GFP, the GFP green fluorescent protein was localized in the nucleus and also distributed in the cell membrane and cytoplasm. In contrast, in protoplast cells transformed with p35S:LhDof4-GFP and p35S:LhDof6-GFP, the LhDof4-GFP and LhDof6-GFP fusion proteins were primarily localized in the nucleus (Figure 8). Plants 13 02009 g008 Figure 8. Subcellular localization of LhDof4 and LhDof6. The first panel shows the bright field. The second panel shows green fluorescence (GFP). The third panel is a merged image of the green fluorescence and bright field. (A) p35S:GFP, (B) p35S:LhDof4-GFP; (C) p35S:LhDof6-GFP. Bar = 20 μm. 2.9. Overexpression of LhDof6 Improved the Cold Tolerance of Liriodendron Hybrid Given that our expression analysis indicates that the LhCDFs gene may play a role in plant response to cold stress, we further investigated its molecular function in this context. We analyzed the response of LhDof6 overexpression plants to abiotic stress and characterized their phenotypes. We constructed overexpression vector (Figure S1). Using qRT-PCR, we selected two overexpression lines (Figure 9) and obtained LhDof6-OE plants through somatic embryogenesis (SE) technology of Liriodendron hybrid. We then compared the freezing stress tolerance of LhDof6-OE transgenic plants grown in 1/2 MS medium to that of the wild type. The results indicate that plants overexpressing LhDof6 exhibit enhanced cold tolerance. Plants 13 02009 g009 Figure 9. The Relative expression of LhDof6 in wild-type, no-load and transgenic plants was determined by using 18S rRNA as the reference gene. * on the bar chart indicates that the difference between the two control groups reached a significant level, that is, p < 0.05, *** means p < 0.001, **** means the difference between the two control groups reached a very significant level, that is, p < 0.0001. In each group, 28 similarly grown Liriodendron hybrid plants were selected and acclimated at 4 °C (16 h light and 8 h dark) for 7 days. They were then subjected to −20 °C for 20 min. After the treatment, the plants were transferred to a greenhouse at 22 °C for a 7-day recovery period. Photos were taken on the first and seventh days of recovery, and survival rates were calculated on the seventh day. The results showed that the leaves of both the overexpression group and the control group exhibited wilting on the first day of recovery (Figure 10). After 7 days, the leaves of the control group did not recover from wilting; most of them began to wither and die. In contrast, the overexpression group showed significant alleviation of leaf wilting, with leaves resuming spreading and exhibiting a low degree of mortality. Survival rate statistics indicated that the overexpression lines had a survival rate of 68–71%, compared to only 25% in the wild type (Figure 10). Plants 13 02009 g010 Figure 10. LhDof6-OE seedling freeze stress treatment and survival rate statistics. (A) Seedlings grown under greenhouse conditions for six months; (B) After −20 °C treatment for 30 min followed by 1 d of recovery cultivation; (C) After −20 °C treatment for 30 min followed by 7 d of recovery cultivation; Survival rate statistics are displayed on the right. The occurrence of low temperature stress in plants is widely recognized to induce numerous physiological and metabolic rearrangements, which are mediated by known determinants. Therefore, we conducted measurements on a range of physiological indicators to further elucidate the role of LhDof6 under freezing stress, as depicted in (Figure 11). After exposure to freezing stress, the MDA content exhibited an increase in both plant types; however, the rise was more pronounced in the wild type, indicating that low temperatures can induce damage to plant cell membranes to some extent, with the wild type experiencing more severe impairment (Figure 11A). Overexpression of LhDof6 leads to a modest increase in plant proline levels, while under freezing stress conditions, the overexpression lines exhibit significantly enhanced proline accumulation compared to the wild type. This accumulation of proline plays a crucial role in safeguarding plant cells (Figure 11C). Plants 13 02009 g011 Figure 11. Measurement of various physiological indicators of seedlings treated at −20 °C for 0 and 30 min: (A) malondialdehyde (n = 9); (B) superoxide dismutase activity (n = 9); (C) proline content (n = 9); (D) hydrogen peroxide (n = 9). * on the bar chart indicates that the difference between the two control groups reached a significant level, that is, p < 0.05; **** means the difference between the two control groups reached a very significant level, that is, p < 0.0001. The level of H2O2 increases rapidly, which can function as a signaling molecule to activate the expression of cold resistance-related genes in plants. However, excessive accumulation of H2O2 may lead to a spike in intracellular ROS levels and result in oxidative stress. Under normal circumstances, the overexpression of LhDof6 resulted in an increase in SOD enzyme activity in Liriodendron hybrid. Subsequently, exposure to cold temperatures further amplified the SOD enzyme activity, with consistently higher levels observed in the overexpression lines compared to the wild type (Figure 11B). Under freezing stress, wild-type plants exhibited excessive accumulation of H2O2, which can lead to irreversible damage to the plants (Figure 11D). The physiological changes observed before and after exposure to cold stress further validate the ability of LhDof6 to enhance the cold tolerance of Liriodendron hybrid seedlings, thereby increasing their survival rate in extreme environments. These analyses underscore the potential beneficial role played by the LhDof6 gene in facilitating plant adaptation to cold stress. 3. Discussion The Dof transcription factors possess a highly conserved single-finger domain, known as a zinc finger domain, consisting of 52 amino acid residues. This specific domain facilitates precise binding to DNA [34]. In this study, we identified 17 Dof genes in the L. chinense genome, designated as LcDof117 based on their chromosomal location. The number of LcDof genes is relatively small compared to some higher angiosperms, i.e., Arabidopsis thaliana (36), Oryza sativa (30), Sorghum bicolor (28), Glycine max (78), Hordeum vulgare (21), Nicotiana tobacum (17) and Populus trichocarpa (43) [35]. The findings suggest a potential association between the number of Dof gene family members and their evolutionary states as well as patterns of family expansion. The protein length, molecular weight (MW), and isoelectric point (pI) of LcDof proteins exhibit significant variations among gene families, indicating their structural diversity and potential adaptation to diverse environments. These characteristics suggest that LcDofs may possess distinct biological regulatory functions in different environments or conditions, particularly under abiotic stress. Furthermore, the variability in length, molecular weight, and isoelectric point of Dof proteins across species implies divergent biological functions of Dof genes between different organisms. This divergence may be attributed to differences in physicochemical properties and spatial structures of these proteins. The collinearity analysis revealed six pairs of gene duplication events in the LcDof genes, with only one pair attributed to tandem duplication, while the remaining pairs were a result of segmental duplication. The fact that all duplicated genes belonged to the same category suggests that L. chinense has undergone at least one whole-genome duplication event since its divergence. The chromosomal localization analysis of the LcDof genes revealed an uneven distribution pattern, with most genes located at the terminal regions of 11 chromosomes and no more than two genes per chromosome. This spatial arrangement suggests active expression of the Dof gene family [28]. The involvement of several transcription factors (TFs) in plant stress responses has been identified, with some participating in intricate regulatory networks. These TFs are predominantly encoded by polygenic families that have undergone multiple rounds of gene replication throughout the evolution of land plants [36,37,38]. The clustered distribution of LcDof genes at the chromosome termini may be associated with the typically more accessible chromatin structure in these regions, which could facilitate active gene expression. Conversely, the chromatin structure near the centromere is generally more compacted, potentially constraining gene expression [39]. The regulation of plant hormones is achieved through the binding and coordinated interaction of various transcription factors with cis-acting elements present in the promoters of plant hormone response genes [40]. The Dof proteins function as regulators of plant hormone response genes and have been demonstrated to mediate the gibberellin response [41]. The Dof transcription factor also exhibits circadian rhythms and plays a pivotal role in perceiving plant photoperiods and regulating flowering time. Analysis of JcDof1 and JcDof3 in Jatropha curcas seedlings revealed their expression patterns under long-day, short-day, and continuous light conditions, as well as their interaction with F-box proteins to modulate photoperiodic flowering [42]. The cis-elements present in the promoters of LcDof genes predominantly consist of light-responsive elements, plant hormone regulatory elements, stress-related elements, and growth and development regulatory elements. This implies that the LcDof genes exert comparable effects on photoperiodic response, abiotic stress response, and plant hormones. Temporal transcriptome analysis of Liriodendron hybrid under low-temperature, high-temperature, and drought conditions revealed that several LcDof genes exhibited a transcriptional profile characterized by an initial upregulation followed by downregulation in response to low-temperature conditions. Notably, the expression dynamics of LhDof4 and LhDof6 were particularly remarkable. Quantitative Real-Time PCR (qRT-PCR) analysis demonstrated that the expression levels of LhDof4 and LhDof6 increased approximately tenfold within 12 to 24 h after exposure to low temperatures compared to their pre-treatment levels. This strongly suggests that these two genes may play a pivotal role in the physiological response of Liriodendron hybrid to low temperatures. The Cycling Dof Factor (CDF) is capable of regulating various aspects of plant growth and development, including the photoperiodic control of flowering as well as root and shoot growth. While most functional characteristics of CDFs have been extensively studied in Arabidopsis, recent data indicate that their diverse roles also extend to other plant species [42,43,44,45]. The role of DOF transcription factors (TFs) has been extensively investigated in numerous plant species, including important crops such as maize, wheat, rice, potato, and bananas, in response to various environmental stress conditions [46,47]. Transcriptomic analysis has revealed a limited overlap in the stress response genes regulated by GI and CDF3, indicating that these two proteins have distinct functions specifically under low temperature and osmotic stress conditions [44]. Further comprehensive and functional analyses are imperative to elucidate the precise roles of these factors in plant responses to diverse environmental stress conditions. Moreover, it has been reported that overexpressing AtCDF3 or SlCDF3 in Solanum lycopersicum enhances tolerance to salt stress [33]. These reports suggest that SlCDFs may have a crucial regulatory function in the upstream pathways of salinity and drought response, similar to their counterparts in Arabidopsis. Furthermore, the overexpression of LhDof6 significantly mitigated the mortality rate of Liriodendron hybrid seedlings exposed to extreme temperatures as low as −20 °C. Physiological changes observed before and after cold stress exposure also provide evidence that LhDof6 can enhance the cold tolerance of Liriodendron hybrid seedlings, thereby increasing their survival rate in harsh environments. These analyses indicate an active role for the LhDof6 gene in facilitating plant adaptation to low temperature stress. 4. Materials and Methods 4.1. Identification of Dof Gene in L. chinense To identify the Dof gene in L. chinense, 37 typical and atypical Dof protein sequences of Arabidopsis thaliana were downloaded from the TAIR database (https://www.arabidopsis.org/Blast/index.jsp, accessed on 21 November 2021). From the pfam website (http://pfam-legacy.xfam.org/, accessed on 22 November 2021), the Dof hidden Markov number is PF02701, and this number has been used as the query condition. Blastp and HMMER were used to query the target sequence in the L. chinense protein database, and candidate sequences were obtained. Based on the Dof conserved domains, CDD-search (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi, accessed on 22 November 2021) was used to check the conserved domains of candidate sequences to further screen out redundant sequences, and finally obtain the target sequence. Gene properties, including length, molecular weight, and isoelectric point of each protein, were determined using the ExPASy website (https://web.expasy.org/protparam, accessed on 3 January 2022) tool. Subcellular localization of LcDof genes was predicted by Cell-PLoc 2.0 (www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2, accessed on 3 January 2022). 4.2. Phylogenetic Analysis and Conserved Domains and Gene Structure Analysis Arabidopsis thaliana, Amborella trichopoda, Zea mays, and Oryza sativa Dof protein sequences used to construct the phylogenetic tree were downloaded from Phytozome (https://phytozome-next.jgi.doe.gov, accessed on 3 January 2022). Multiple sequence alignment of Dof gene family members was performed using MAFFT software (https://mafft.cbrc.jp/alignment/software, accessed on 25 January 2022) with default parameters. MAGE 7.0 was utilized to construct the phylogenetic tree, employing the neighbor-joining method with a bootstrap value of 1000 to analyze the evolution of the Dof gene in L. chinense. The gene structure information of each LhDof6 gene was acquired from the genomic feature file (GFF3) and displayed using Tbtools software (https://github.com/CJ-Chen/TBtools/releases, accessed on 12 February 2022), while the chromosomal location and microsynteny of LhDof6 were visualized using the Tbtools software. The Multiple Collinearity Scan toolkit (MCScanX) program was used to verify putative paralogous genes (blast hits E-value cutoff < 1 × 10−6, collinearity >  70%). The cis-acting elements of LhDof6 genes were analyzed by PlantCARE (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/, accessed on 15 February 2022) and displayed using TBtool software. The conserved motifs of LcDof proteins were predicted using MEME (v5.4.1) (https://meme-suite.org/meme/tools/meme, accessed on 21 February 2022) with the following settings: the discovery mode was classic, site distribution was zero or one occurrence per sequence, the background is 0-order background model, the maximum number of different motifs: 20, minimum motif width: 6, and maximum motif width: 50, and displayed using the TBtool software. 4.3. Plant Materials and Genetic Transformation of Liriodendron Hybrid Liriodendron hybrid seedlings generated through somatic embryogenesis (SE) were used as the starting material throughout this study [48]. Before any experiments were performed, plantlets were taken out of the culture medium vessel and acclimatized in a greenhouse for 2 weeks (22 °C, long day photoperiod of 16 h light/8 h dark and 75% relative humidity). For various abiotic stress treatments, plants were transferred to a growth chamber (long time photoperiod of 16 h light/8 h dark and 75% relative humidity): to simulate cold or heat or drought stress, plantlets were subjected to a 4 °C or 15% PEG6000 treatment, respectively, for 1 h, 6 h, 12 h and 1 d in the growth chamber [49]. The full-length coding sequence (CDSs) of LhDof6 was amplified from Liriodendron hybrid by PCR, and cloned into the pBI121 vector with overexpression of LhDof6 (hereinafter referred to as LhDof6-OE) under the control of CaMV 35S promoter and ScaI and XbalI, respectively. After the vector was constructed, the vector was transformed into Agrobacterium receptive cell EHA105, and the monoclonal colony was selected to verify its correctness before transfection [48]. Positive callus was obtained through genomycin screening, and mutants identified via PCR and sequencing were used for subsequent experiments. 4.4. RNA Extraction and qRT-PCR Analysis Based on the transcriptome data of Liriodendron hybrid under different abiotic stresses, a heatmap of LhDof6 gene expression was generated using Tbtools. The transcriptome data used in this study has been archived and can also be obtained on the NCBI website, cold and heat stress accession numbers were PRJNA679089 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA679089/, accessed on 19 June 2022), and drought stress accession number was PRJNA679101 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA679101/, accessed on 19 June 2022). RNA degradation and contamination were monitored on 1% agarose gel. RNA purity was detected using the NanoPhotometer® spectrophotometer (IMPLEN, Westlake Village, CA, USA). Using the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA), The RNA Nano 6000 Assay Kit (Agilent Technologies, CA, USA) assesses RNA integrity and synthesizes cDNA using the HiScript® III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China), using the extracted RNA as a template. Using primer3 website (https://www.yeastgenome.org/primer3, accessed on 30 June 2022) design quantitative expression LcDofs primers, RT-qPCR using Roche Lightcyler® 480 instrument II, use 2× AceQ® qPCR SYBR® Green Master Mix (Without ROX) (Vazyme, Nanjing, China). The PCR mixture consists of 2× AceQ® qPCR SYBR® Green Master mix (Without ROX) 10 μL, each primer 0.4 μL, cDNA template 1 μL (10 ng/μL), 8.2 μL ddH2O added, the final volume was 20 μL. The internal reference gene was L. chinense 18S gene. The reaction process is as follows: 95 °C—10 min, 95 °C—10 s, 57 °C—30 s, 40 cycles. All reactions were performed in 96-well plates. Biological replicates were performed for each reaction, as well as three technical replicates. All data generated by real-time PCR amplification were analyzed by 2−ΔΔCT [50]. 4.5. Subcellular Localization To verify the subcellular localisation of LhDof4 and LhDof6, we obtained the LcDof4 and LcDof6 target fragment by PCR using the LhDof4 and LhDof6 sequence as the reference sequence and the cDNA of Liriodendron hybrid seedling as the template. The plasmid pJIT166-GFP was digested with XbaI and BamHI enzymes, and the linear vector fragment was ligated with the target gene fragment to construct the p35S:LhDof4/6-GFP fusion expression vector. Plasmids were extracted using an endotoxin-free plasmid extraction kit (TIANGEN, Beijing, China). The callus of Liriodendron hybrid cultured for 20 days was used to prepare protoplasts, and protoplasts were slowly and gently dissolved into a solution (10 mL) containing 0.5 M mannitol, 20 mM MES, pH 5.7, 20 mM KCl, 0.1% (w/v) bovine serum albumin, 10 mM CaCl2 and digested at 28  °C under dark conditions for 3 h [51], the protoplasts were transformed by PEG6000, pipetted into a 6-well cell culture plate, and cultured at 23 °C under dark for 1648 h [51], then the fluorescence effect of protoplasts was observed by ZEISS LSM 800 fluorescence microscope (Carl Zeiss, Oberkochen, Germany). 4.6. p35S: LhDof6 Positive Seedlings and Cold Stress Tolerance Assay LhDof6-OE seedlings generated through SE. Each group selected 28 uniformly growing and healthy 6-month-old Liriodendron hybrid seedlings for a freeze tolerance test. The seedlings were exposed to −20 °C for 20 min and then transferred to a greenhouse environment (26 °C with 16 h of light/8 h of darkness) for observation. After the treatment, the plant leaves were washed with distilled water, dried with paper towels, weighed, and then flash-frozen in liquid nitrogen before being stored at −80 °C for future use. The assay kits for hydrogen peroxide (H2O2), lipid peroxidation malondialdehyde (MDA), proline (PRO), and superoxide dismutase activity (SOD) were purchased from Nanjing Jiancheng Bioengineering Institute, Jiangsu provenience, China. 4.7. Data Analysis Data were plotted using Prism 8.0 software, and the significance of differences between samples was assessed using t-tests and one-way ANOVA. * on the bar chart indicates that the difference between the two control groups reached a significant level, that is, p < 0.05; ** means p < 0.01, *** means p < 0.001, **** means the difference between the two control groups reached a very significant level, that is, p < 0.0001. The content of malondialdehyde, superoxide dismutase activity, proline content and hydrogen peroxide in plants before and after freezing stress were significantly analyzed. 5. Conclusions Our research findings indicate that the L. chinense genome harbors a total of 17 Dof genes. The physicochemical properties of the proteins encoded by these genes exhibit considerable variation. Specifically, the protein lengths span from 160 to 635 amino acids, while their molecular weights range between 17.01 and 71.60 kDa. The majority of LcDof proteins are categorized as unstable, rendering them susceptible to alterations caused by external environmental factors. The DOF domains within the LcDof gene family exhibit complete conservation. The six clades are characterized by distinct motif structures and gene architectures, implying potential functional similarities among LcDof genes within each clade. Phylogenetic analysis revealed that the Dof genes can be classified into eight distinct subfamilies, with the notable absence of LcDof genes in the C3 subfamily. Collinearity analysis suggests that segmental duplications are primarily responsible for the expansion of the LcDof gene family, which underwent five such events along with one tandem duplication event. Analysis of promoter regions also indicates a rich presence of cis-acting elements. The elements are classified into four categories: growth and development-related elements, light-responsive elements, abiotic stress-responsive elements, and plant hormone-responsive elements. Moreover, there is a relatively abundant presence of abiotic stress-responsive elements and plant hormone-responsive elements. The transcriptome data of Liriodendron hybrid under various abiotic stresses reveals a high expression level of Dof genes from the D1 subfamily, particularly highlighting the significant responses of LcDof4 and LcDof6. This finding was further confirmed through RT-qPCR analysis, suggesting that LcDof4 and LcDof6 may play a crucial role in positively regulating the response to cold stress. We successfully cloned the LcDof4 and LcDof6 genes, and observed their expression in the cell nuclei of protoplasts derived from Liriodendron hybrid callus. Furthermore, we conducted overexpression experiments with LhDof6 in Liriodendron hybrid, which resulted in a significant increase in the survival rate of six-month-old seedlings under −20 °C conditions. Furthermore, a series of physiological measurements have confirmed that the overexpression of LhDof6 significantly enhances the plant’s cold tolerance. In summary, we have successfully identified and analyzed the Dof gene family in L. chinense for the first time, screened for genes associated with cold tolerance, and conducted an initial functional analysis on the role of LhDof6 in enhancing cold tolerance.

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Abstract Chloroplasts is the site for photosynthesis, which is the main primary source of energy for plants. Golden2-like (GLK) is a key transcription factor that regulates chloroplast development and chlorophyll synthesis. However, most studies on GLK genes are performed in crops and model plants with less attention to woody plants. In this study, we identified the LhGLK1 and LhGLK2 genes in the woody plant Liriodendron hybrid, and they are specifically expressed in green tissues. We showed that overexpression of the LhGLK1 gene improves rosette leaf chlorophyll content and induces ectopic chlorophyll biogenesis in primary root and petal vascular tissue in Arabidopsis. Although these exhibit a late-flowering phenotype, transgenic lines accumulate more biomass in vegetative growth with improved photochemical quenching (qP) and efficiency of photosystem II. Taken together, we verified a conserved and ancient mechanism for regulating chloroplast biogenesis in Liriodendron hybrid and evaluated its effect on photosynthesis and rosette biomass accumulation in the model plant Arabidopsis. Keywords: LhGLK1; ectopic chloroplasts biogenesis; chlorophyll; photosynthesis; Liriodendron hybrid

1. Introduction Chloroplasts are semi-autonomous organelles of plants whose biogenesis depends on the co-expression of nuclear and plastid genes [1]. It is widely believed that the origination of chloroplasts followed an endosymbiotic event between a photosynthetic cyanobacterium and a eukaryote [2]. In the process of evolution, most genes in cyanobacteria were gradually transferred to the host genome [3]. With the germination of seeds, chloroplasts are originally transformed from proplastids, which are colorless organelles without chlorophyll or photosynthesis [3]. As signals, light and plant hormones participate in the biogenesis of chloroplasts [4]. Subsequently, nuclear-coded photosynthetic components are synthesized in the cytoplasm and imported to the chloroplast, which are precisely targeted to protein complexes in photosystems and thylakoids that are coded by the plastid genome. The mechanism of regulating this process has been described before [3,5]. In addition, during the lifetime of a plant, variation in sunlight intensity and stress may have an impact on chloroplast biogenesis. After a long evolution, plants have formed a regulatory network to adapt to changes in the environment [6]. By accepting retrograde signals from the plastid, plants coordinate nuclear photosynthetic gene expression and growth conditions to sustain the normal function of chloroplasts [6]. For most photosynthetic plants, only a few transcription factors that function in regulating plant chloroplast biogenesis have been reported [7,8]. Over the past few decades, research on the nuclear transcription factor GLK has shown that GLK plays an important role in regulating photosynthesis-associated nuclear genes and then promotes chloroplast biogenesis [9]. GLK (Golden2-like) genes were first discovered and named in the research of maize mutant in 1920s and were found only in plants [10,11,12]. GLK genes belong to the GARP super gene family, which is named after the maize GOLDEN2 gene, Arabidopsis ARR B-class genes, and Chlamydomonas PSR1 [13,14]. Besides their function in regulating chloroplast biogenesis, as mentioned before, other studies indicated that GLK genes also participate in biotic or abiotic stress and leaf senescence [6,15,16,17]. Based on the results of evolutionary analysis, GLK genes existed before the evolution of land plants [2]. A typical GLK protein structure has two conserved domains: the N-terminal DNA binding domain (DBD) and the C-terminal GCT-box [1,18,19]. The DNA binding domain is responsible for binding to downstream target genes, and the DBD domain is sufficient to promote reporter gene transcription in the yeast transactivation trail [13]. GCT-box may play a role in homo- and heterodimerization of the maize G2 and ZmGLK1 proteins, both of which both may be necessary to conduct its function in vivo [13]. GLK proteins are highly conserved in two domains in different species. GLK genes promote chloroplast formation by binding to downstream promoters of photosynthesis-related genes and driving their transcription, which mainly contain genes coding key enzymes in chlorophyll biosynthesis and protein subunits in the photosystem such as HEMA1, GUN4, PORA, CAO, Lhca, Lhcb [20,21]. A previous study of Arabidopsis showed that the target genes of AtGLKs are mainly related to chlorophyll synthesis and light harvesting [20]. Notably, photosynthesis-related genes are the most highly represented and significantly enriched in overexpressing AtGLK lines [20]. Even though the function of GLK1 and GLK2 is highly redundant in Arabidopsis, the differential expression gene sets are slightly different between atglk1 and atglk2 in transcriptome data [20]. In the C4 plant maize, a tissue-specific expression pattern has been observed, which is relative to photosynthetic cell differentiation [13]. Moreover, mutation of ZmG2 impeded the chloroplast biogenesis in bundle sheath cells. In the latest study of maize, ZmGLK1 was not required for chloroplast biogenesis in mesophyll cells. These data showed that although genome replication events lead to the replication of GLK genes, the function of GLK genes may have been partly diverged. In Arabidopsis, the loss of two GLK genes leads to incompletely developed chloroplasts, lower chlorophyll content, and light green phenotype in leaves [1,3]. On the contrary, overexpression of GLK genes will promote chlorophyll biosynthesis and thylakoid stacking in many species, such as Arabidopsis, tomato, rice [22,23,24,25]. Interestingly, GLK genes have an ability to enhance chloroplast biogenesis in non-green tissues. Chloroplasts were observed and tested in overexpressing GLK rice callus and Arabidopsis roots [4,26]. In tomatoes, fruit showed a dark-green phenotype with more chlorophyll content and nutrition accumulation by overexpressing SlGLK genes [23,27]. In addition, GLK protein function is highly conserved in different species. The expression of moss PpGLKs can partly save the phenotype of the double mutant glk1glk2 in Arabidopsis, which is the most distantly species to the angiosperms [2,28]. Even relative research showed that some components of the GLK regulation network evolved independently in different species; this ancient and conserved mechanism may have existed for at least 400 million years and began to evolve before land plants [28,29]. In recent years, with the increasing requirements for agriculture and forestry, studies about GLK genes have shown their great potential in agriculture and forestry breeding. More chlorophyll content and chloroplasts may change the photosynthetic characteristics of plants. Overexpression of ZmGLKs enhanced the photosynthetic rate of rice with increased grain yield. Nevertheless, some studies revealed that excessive chloroplasts and chlorophyll are unfavorable to normal photosynthesis [24]. In some cases, the effect of overexpressing ZmGLKs in rice will appear when plants are cultured under strong light, while no marked difference under moderate light conditions were found [22]. In Arabidopsis, negative results regarding photosynthesis and biomass accumulation were observed in overexpressing AtGLKs lines as transgenic lines showed less rosette biomass accumulation with decreased qP and ΦPSII [7]. These studies indicated that using GLK genes to improve the photosynthetic characteristics of plants is complicated but worthy. Liriodendron hybrid was obtained by artificial hybridization, and its parents are Liriodendron chinense and Liriodendron tulipifera. The genus Liriodendron is an ancient relic group with an evolutionary position between angiosperms and eudicots. The Liriodendron hybrid is famous for timber and ornamental value in China due to its strong heterosis [30,31]. There are many studies about GLK genes in many crops and Arabidopsis. To our knowledge, there are few relevant studies on woody plants besides Birch [32]. By establishing somatic embryogenesis and transgenesis systems, Liriodendron hybrid shows great potential in production and breeding [32]. Here, we identified LhGLK1 and LhGLK2, and overexpressed LhGLK1 in Arabidopsis. Our data confirmed that LhGLKs have highly conserved DBD and GCT domains, in keeping with Arabidopsis and crops. Not surprisingly, the function of LhGLK1 in regulating chlorophyll biosynthesis and chloroplast formation was also observed in Arabidopsis. In addition, vegetative growth and parameters of photosynthesis were improved when LhGLK1 was overexpressed in Arabidopsis. We expand the boundaries of studies about GLK based on crops and explore the possibility of LhGLK1 in improving plant photosynthesis. We present a vision that the application of GLK in woody plants and crops may bring more biomass accumulation and CO2 absorption with increased photosynthetic intensity and decreased photoinhibition [33,34].
2. Results 2.1. Identification and Tissue Expression Pattern Analysis of LhGLKs To identify GLK1 and GLK2 in the Liriodendron hybrid, we used the GLK1 and GLK2 protein sequences from Arabidopsis thaliana, Oryza sativa, Zea mays, Malus domestica and Populus tomentosa as queries to blast against the protein database of Liriodendron chinense, and then designed primers to clone LhGLK genes in Liriodendron hybrid seedlings. The amino acid sequences translated from CDS were aligned with GLK genes of the mentioned species, finding that they contained the conserved DNA binding domain and the C-terminal domain (Figure 1B). Next, a phylogenetic analysis suggested that LhGLKs are more closely related to GLK genes in poplar and apple (Figure 1A). Ijms 25 06968 g001 Figure 1. Identification and expression analysis of LhGLKs. (A) Phylogenetic analysis of GLK1 and GLK2 from Liriodendron hybrid (Lh), Arabidopsis thaliana (At), Oryza sativa (Os), Zea mays (Zm), Malus domestica (Md) and Populus tomentosa (Pt). The tree was constructed on the software MEGA7, using neighbor-joining (NJ) method with 1000 bootstrap replicates. (B) Amino acid sequence alignment of GLK genes from six species. The black lines represent the conserved DNA binding domain and the C-terminal domain. (C) Relative expression analysis of LhGLK1 and LhGLK2 in different tissues of Liriodendron hybrid. Three biological replicates of each tissue were harvested for qRT-PCR experiments. Error bars mean the SD of three biological replicates. Scale bar = 1 cm. Previous studies demonstrated that the GLK1 and GLK2 genes are specifically expressed in green tissues and have different expression patterns. To confirm that, we collected floral organs, leaves, and branch bark from the mature tree and leaves from seedlings of Liriodendron hybrid. The results of real-time quantitative PCR (qRT-PCR) showed that the expression levels of LhGLKs are higher in green tissues such as sepals, petals and leaves. Notably, LhGLK2 is only specifically expressed in leaves and branch bark (Figure 1C). 2.2. Overexpression of LhGLK1 in Arabidopsis Leads to More Chlorophyll Accumulation and Promotes Chloroplast Formation in Non-Green Tissue GLK is a key component in regulating the formation of chloroplasts. Defective chloroplasts were observed in the glk1 glk2 double mutant of Arabidopsis [20]. Here, we generated overexpressed LhGLK1 lines in Arabidopsis, which were driven by the 35S cauliflower mosaic virus promoter (Figure 2A). Then, we examined the transcription level of LhGLK1 in leaves by qRT-PCR. All three transgenic lines showed different overexpression levels compared to control lines (Figure 2C). Rosette leaves of transgenic plants showed a dark-green phenotype after being cultured on soil (Figure 2B and Figure S1). We visualized and decomposed the vision disparity into L, a, b aspects by CIE (Figure S2). Value L, a and b represent color brightness, red-green and yellow-blue, respectively. All three transgenic lines have a decreased absolute value at L, a and b when compared with control lines, which means transgenic lines show dark-green. We believe that the reason for the dark-green phenotype of leaves is due to excessive accumulation of chlorophyll in transgenic lines; so, the total chlorophyll of rosette leaves was extracted and measured. Consistent with dark-green phenotype, chlorophyll content was much higher in OE lines (Figure 2D–F). Because chlorophyll a and b absorb light at different wavelengths, the chlorophyll a/b ratio is a signature of photosynthetic apparatus state; so, the Chl a/b ratio was calculated, which showed no remarkable change in transgenic lines (Figure S3). Ijms 25 06968 g002 Figure 2. Overexpression of LhGLK1 in Arabidopsis promotes Chlorophyll biosynthesis. (A) Schematic diagram of the LhGLK1 overexpression (LhGLK1-OE) vector with CaMV35S promotor and empty vector (EV). (B) The phenotype of dark green leaves in the line #6 of overexpressing LhGLK1, comparing to wild-type (WT) and empty vector (EV). Scale bar = 1 cm. (C) Relative expression levels of three different LhGLK1-OE lines. The vertical axis values represent 2(−ΔCT), and the average ± SE is shown (n = 3 biological replicates). Different letters over the bar represent significant differences based on one-way ANOVA tests (p < 0.05). (D–F) Chlorophyll a content (D), chlorophyll b content (E), and total chlorophyll content (F) (mg/g of fresh weight) in leaves of WT, EV, and three LhGLK1-OE lines (OE6, OE7 and OE8). All the values are means ± SE (n = 5 biological replicates). The letters a, b, c and d over the bar represent significant differences based on one-way ANOVA tests (p < 0.05), with groups marked by identical letters having no significant difference between them. In order to study the effect of LhGLK1 overexpression on chloroplast biosynthesis, we observed the ultrastructure of leaf chloroplast by transmission electron microscopy. As shown in Figure 3, the chloroplast of OE lines exhibits a higher thylakoid number, but no significant difference was found in comparison to WT. Ijms 25 06968 g003 Figure 3. Transmission electron microscopy (TEM) of leaf chloroplast ultrastructure in (A) wild-type and (B) OE-6 and (C) OE-7. ch: chloroplast; W: wall; SG: starch granule. The arrow points to the thylakoid. (D) Quantification of granal stacking in mesophyll cells. Thylakoids were counted in chloroplast of 5 different cells. Scar bar = 1 μm. All the values are means ± SE (n = 5 biological replicates). The letters over the bar represent significant differences based on one-way ANOVA tests (p < 0.05), with groups marked by identical letters having no significant difference between them. Previous studies about GLK genes in Arabidopsis showed that GLK genes have the capacity to induce chloroplast formation in roots. Not all cells have the potential to form a chloroplast, and it is considered that non-photosynthetic cells are regulated by other factors to prevent the formation of chloroplast [3]. In this study, chlorophyll autofluorescence of experimental plants was conducted and observed by confocal microscope [35]. Consistent with previous information, there is strong chlorophyll autofluorescence in the middle of primary roots of OE lines (Figure 4A,B). At the same time, WT and EV lines showed no fluorescence signals in root cells, regardless of cell type. Similar cases were observed in petals of transgenic lines. Although chlorophyll autofluorescence was present in the bottom of the petal in all lines, the fluorescence signal disappeared suddenly in the upper vascular bundle of the petal in WT and EV lines. On the contrary, the fluorescence signal was still sustained in the upper vascular bundle of the petal in transgenic lines (Figure 4C,D). Ijms 25 06968 g004 Figure 4. Overexpression of LhGLK1 leads to ectopic chlorophyll biosynthesis in primary root and petal vascular tissue in Arabidopsis. (A,B) The chlorophyll (Chl) autofluorescence confocal micrographs of the primary root in wild-type and LhGLK1-OE6 seedlings, respectively. Scale bar = 50 µm. (C,D) The chlorophyll autofluorescence confocal micrographs of the petal in wild-type and LhGLK1-OE6 Arabidopsis lines, respectively. Scale bar = 1 mm. All the Chl micrographs above was scanned under 488 nm laser excitation. Furthermore, we tested the transcription levels of LhGLK1 and GLK target genes in roots by qRT-PCR. As expected, the expression levels of GLK target genes were up-regulated compared to those of WT (Figure 5D–F). Nevertheless, the expression levels of the mentioned genes were down-regulated in leaves (Figure 5A–C). Conversely, Western blot shows that there are more Lhca1 and Lhcb3 proteins in OE leaf (Figure 6). Increased protein levels may reduce the expression of LHCA1 and LHCB3. Enhanced expression of photosynthesis-associated nuclear genes may contribute to the biogenesis of chlorophyll in the root. In conclusion, the above data are consistent with GLK genes’ function, which positively regulate chloroplast and chlorophyll biosynthesis in green and some specific non-green tissues. Ijms 25 06968 g005 Figure 5. Relative expression levels of chloroplast biogenesis and chlorophyll synthesis genes in leaves (A–C) and roots (D–F) of wild-type and LhGLK1 overexpression lines analyzed by qRT-PCR. All the values are means ± SE (n = 3 biological replicates). Different letters over the bar represent significant differences based on one-way ANOVA tests (p < 0.05), with groups marked by identical letters having no significant difference between them.. Ijms 25 06968 g006 Figure 6. Western blot analysis of photosynthetic proteins. Photosynthetic proteins in 10 mg of total membrane protein from leaf samples of wild type and OE lines. The values represent relative protein content to GAPDH. 2.3. LhGLK1 Overexpression Changes Photosynthetic Characteristics and Flowering Time and Improved Growth Improving photosynthetic efficiency was regarded as a viable way to enhance grain yield potential in crops [22]. Many studies focusing on the influence of GLK on plant photosynthetic characteristics and grain field have been conducted. Excessive chlorophyll and chloroplast may have impacts on plant photosynthetic intensity and photoinhibition. In our study, we measured chlorophyll fluorescence parameters of experimental plants to analyze the variations in photosynthetic characteristics in transgenic lines. As shown in the figure, the efficiency of photosystem II (ΦPSII) is enhanced in OE lines, which means OE lines are more efficient in using energy for photosynthesis (Figure 7G). At the same time, photochemical quenching (qP) and qL were significantly increased in OE lines, which shows that more energy was used for photosynthesis instead of heat (Figure 7I,J). Nevertheless, we did not observe any significant changes in maximum quantum efficiency of photosystem II (Fv/Fm) (Figure 7F). In addition, parameter non-photochemical quenching (NPQ) and non-photochemical quenching (qN) represent the fraction of regulated heat dissipation. NPQ and qN are slightly higher in OE lines (Figure 7H,K). This parameter implies the ability of photoprotection. Ijms 25 06968 g007 Figure 7. Overexpressing LhGLK1 changed photosynthetic characteristics and delayed flowering time of Arabidopsis and affected its growth. (A) The late-flowering phenotype of LhGLK1-OE lines compared to wild-type and empty vector control Arabidopsis. Scale bar = 1 cm. (B) The flowering time (as days after sowing) of WT, EV and three OE lines. (C) The flowering time (as number of rosette leaves) of WT, EV and tree OE lines. (D) The rosette fresh weight of WT, EV and two OE lines. (E) The rosette dry weight of WT, EV and two OE line. (F–K) The chlorophyll fluorescence characteristics of WT, EV and OE lines. Different letters over the bar represent significant differences based on one-way ANOVA tests (p < 0.05), with groups marked by identical letters having no significant difference between them. Overexpressing AtGLK1 in Arabidopsis exhibited late-flowering phenotype, whose number of rosette leaves and flowering time delay were significantly increased compared to WT [7]. As with the previous note, flowering time delay was observed in OE lines with more rosette leaf number and rosette biomass accumulation (Figure 7A–C). In addition, OE lines exhibit higher fresh and dry weight of rosette leaves compared to the control lines (Figure 7D,E).
3. Discussion Chloroplast biogenesis in land plants is critical and necessary for the ecological system. Nearly all energy in the ecosystem comes from photosynthesis. Normal chloroplast biogenesis relies on photosynthesis-associated nuclear and plastid genes. Some researchers deem that there are main controllers to regulate chloroplast biogenesis in plants [7,36]. Recently, transcription factor GLK and GNC showed great function in regulating chloroplast biogenesis. Due to woody plants having longer life cycles and more difficult transgenesis systems, few experiments were performed. Here, we found that GLK proteins have conserved DBD and GCT domains, regardless of whether in crops or woody plants. We then mainly generated ectopic overexpression of LhGLK1 lines to verify its function in promoting chloroplast formation and evaluating its improvement to photosynthesis and growth. First, we observed that LhGLK1 not only enhanced leaf chlorophyll content but also promoted chloroplast formation in non-green tissues. Interestingly, we did not observe chloroplast in root epidermis cells. Chloroplasts were distributed mainly in the middle of the primary root and petal vascular bundle. This is not a new discovery, as it was previously observed in many similar studies [3]. We assume that other factors prevent chloroplast formation based on cell type and its specific gene expression pattern. In a similar study of overexpressing AtGLKs in Arabidopsis, chlorophyll autofluorescence was also tested in the root of WT, which had fewer chloroplasts in comparison to transgenic lines [26]. In our study, we did not test any chlorophyll autofluorescence in root cells of WT. The authors of the aforementioned study performed chlorophyll autofluorescence when plants were cultured for 21 days, but in our study, we used 8 days. This situation indicated that the mature root of Arabidopsis has the capacity to synthesize chlorophyll and chloroplasts when cultured in light conditions, but not the seedling root. Overexpressing LhGLK1 in Arabidopsis may bring this event forward. Next, GLK target genes are mainly associated with the photosystem complex and chlorophyll synthesis. Due to OE lines exhibiting a dark-green phenotype and containing more chlorophyll content, we conducted qRT-PCR to test the transcription levels of GLK target genes in rosette leaves, which were previously reported by ChIP in Arabidopsis [20]. In opposition to what was expected, the transcription levels of most tested genes were not up-regulated in OE lines when compared to the control lines in the leaf. That was consistent with previous studies in Arabidopsis [17,20]. Transcription levels of most AtGLK1 downstream target genes have no remarkable difference in comparison to WT in a microarray analysis of a GLK1-overexpressing line, which is based on a wild-type background. Particularly, the glk1 glk2 double-mutant background is opposite to the above situation [20]. When AtGLK1 is overexpressed in the glk1 glk2 double mutant, nearly all transcription levels of downstream genes were significantly up-regulated. We believe that a complicated regulation pathway in the WT background may alleviate the effect of overexpressing LhGLK1. Mark T. Waters et al. provided a detailed explanation [20]. But we observed ectopic chloroplasts biogenesis in root of transgenic lines, while no chloroplast in WT. All things considered, we assumed that constitutive overexpression may have a significant impact on the root. As expected, the expression levels of tested genes were up-regulated in the root. This study is partly consistent with the above study and explanation. Finally, a previous experiment about overexpressing AtGLK1 in Arabidopsis showed that the efficiency of photosystem II (ΦPSII) and photochemical quenching (qP) of transgenic lines were decreased in comparison to WT [7]. At the same time, the shoot biomass of AtGLK overexpressing lines was also decreased, which is opposite to our study. We presumed that this was the result of heterologous expression or the transcription level of LhGLK1. In their study, there was a great discrepancy between WT and OE lines in rosette fresh weight and leaf area. Normal biomass accumulation was dramatically decreased in OE lines, which was consistent with the data of qP and ΦPSII. Conversely, we did not observe a markedly suppressed value in rosette leaf area in overexpressing LhGLK1 lines at the same time, besides the late-flowering phenotype. Furthermore, we do not suppose that the great discrepancy between these two similar experiments was entirely caused by culture condition. We know that ectopic overexpression of maize ZmGLKs enhanced rice’s photosynthetic intensity and grain field in rice. Similar to the above situation, other similar research showed that excessive expression of ZmGLKs lines generated fewer seeds weight when compared with WT in rice, in which ZmGLKs were driven by a mild promoter [22,24]. Moreover, the culture conditions may have an impact on the effect of overexpressing GLK genes. Differences between overexpressing ZmGLKs lines and WT may appear when they are cultured under fluctuating light conditions, even though there is no discrepancy in stable light conditions. Overexpressing ZmGLKs may alleviate photoinhibition when plants are cultured in strong light or fluctuating light intensity conditions. According to studies in Arabidopsis, the function of AtGLKs primarily influences the genes related to light harvesting and chlorophyll biosynthesis, instead of photosynthetic genes. Consequently, we assume that the overexpression of LhGLK1 gene indirectly influences photosynthesis in Arabidopsis. In conclusion, we found that heterologous expression of LhGLK1 in Arabidopsis caused chloroplast and chlorophyll biosynthesis in the root and rosette leaf, respectively, which is consistent with the function of GLK. We proved that overexpression of LhGLK1 in the root drove the expression of genes related to photosystem and chlorophyll biosynthesis. In addition, leaf chlorophyll fluorescence in qP and ΦPSII increased with more biomass accumulation of rosette leaf. We still do not have any detailed explanations about the cause of changes in chlorophyll fluorescence parameters. The application of GLK may enhance timber yield and grain yield in practical production. Changes in leaf color may lead to new varieties of ornamental plants.
4. Materials and Methods 4.1. Plant Materials and Growth Conditions All experimental Arabidopsis thaliana is Columbia ecotype-0 (Col-0). Wild-type (Col-0) and T3 generation transgenic lines seeds were cultured on 1/2 MS medium without antibiotic selection. After 3 days of vernalization, all seeds were germinated in an illumination incubator for 8 days. Then, all genotype plants were transplanted onto soil. All days mentioned in the article refer to the days cultured on soil after 8 days of germination. Growth conditions are 23 °C with a long day 16-h light/8-h dark cycle and 70% humidity. Light intensity at 6500 lux. 4.2. Generation of Overexpressing LhGLK1 Lines To generate the p35S:LhGLK1 vector, total RNA from the leaves of 90-day-old Liriodendron hybrid seedling was extracted and used for LhGLK1 cDNA synthesis. Then, LhGLK1 cDNA was cloned by Phanta DNA Polymerase (Vazyme, P505-d1/d2/d3, Nanjing, China) using gene-specific primer pairs. LhGLK1 cDNA was then connected to the pBI121 vector by the method of homologous recombination. The p35S:LhGLK1 vector was transformed into the Agrobacterium strain GV3101 for floral dip transformation based on Arabidopsis thaliana Columbia-0 (Col-0). Transgenic plants were screened on 1/2 MS medium with kanamycin (50 mg/L) until generation T3. DNA of all transgenic plants was extracted for PCR to confirm the positive plant using T-DNA and gene-specific primer pairs. 4.3. Identification of the LhGLK Genes To identify the LhGLK genes, we downloaded GLK1 and GLK2 protein sequences of Arabidopsis thaliana, Oryza sativa, Zea mays, Malus domestica and Populus tomentosa from Phytozome v13 (https://phytozome-next.jgi.doe.gov/) (accessed on 6 December 2023). These were used as queries to blast against the protein database of Liriodendron. The candidate sequences were then aligned with the mentioned GLK genes by DNAMAN 6.0, followed by phylogenetic analysis with the neighbor-joining (NJ) method (1000 bootstrap replicates). 4.4. Transmission Electron Microscopy Cut the rosette leaves (30 day) into 1 mm wide pieces with a blade, fix them in front of 4% glutaraldehyde (prepared with 0.2 Mol phosphate buffer pH 7.2), and permeate with weak vacuum. After cleaning with phosphate buffer for 3 times (20 min each time), fix with 2% w/v osmium tetroxide (prepared with 0.2 Mol phosphate buffer pH 7.2) and let these stay overnight. After cleaning 3 times with phosphate buffer (20 min each time), dehydrate with 30%, 50%, 70%, 90% concentration of acetone step by step, dehydrate with 100% acetone twice, for 30 min each time at each stage. The embedding agent is infiltrated step by step, embedded, and polymerized overnight in the temperature range of 37 °C–45 °C–60 °C in the incubator. Us RMC ultra-thin microtome for ultra-thin section, section thickness 50–70 nm. Dye with 0.5% w/v uranium acetate, 0.2% w/v lead citrate, rinse with deionized water. Observe and photograph the stained sections with a JEM-1400 transmission electron microscope (JEOL, Tokyo, Japan). 4.5. qRT-PCR Analysis Liriodendron hybrid flower tissues derived from an adult tree in Nanjing Forestry University (Nanjing, China) were used. Arabidopsis tissues came from the largest rosette leaves of experimental plants. The roots were derived from seedlings cultured on medium for 20 days. All tissues were saved in liquid nitrogen. Then, total RNA was isolated by RNA extraction kit (Promega, LS1040, Shanghai, China). RNA was reversed into cDNA to conduct Quantitative real-time PCR (qRT-PCR) using Vazyme AceQ qPCR SYBR Green Master Mix (without ROX) (Q121-02) on a LightCycler 480 II (Roche, Basel, Switzerland) and specific primer pairs in Table S1. The result was normalized with Liriodendron hybrid 18S and Arabidopsis Actin-2 as a reference, respectively. 4.6. Western Blot Analysis The total protein of leaf (30-day) was extracted as described [37]. Then, 10 µg protein of each line was used for polyacrylamide gel electrophoresis, and electrotransferred onto nitrocellulose membranes. Then, the specific antibodies of Lhca1 and Lhcb3 were used for reacting with a protein band, which was detected by chemiluminescence reagent. ImageJ 1.8.0 was used to calculated gray value of band. Antibodies and relative reagent were applied by ORIZYMES (Shanghai, China). 4.7. Chlorophyll Content and Measurement The materials came from rosette leaves of all lines when seedlings were cultured on soil for 15 days. Rosette leaves chlorophyll of each genotype was extracted complying with the protocol of the test kit (NJJC, A147-1-1,Nanjing, China). Leaves were ground using quartz sand. Then, chlorophyll was extracted by a mixture of acetone and ethanol (1:1). Chlorophyll content was measured on a microplate reader (Molecular Devices, SpectraMax M3, Sunnyvale, CA, USA) at 645 nm and 663 nm. Chlorophyll a and chlorophyll b were calculated as described [38]. 4.8. Chlorophyll Autofluorescence Analysis Chlorophyll autofluorescence between 660 and 700 nm was detected under 488 nm laser excitation and merged with the bright field based on confocal Zeiss LSM 800 (Oberkochen, Germany). Smart setup mode was used to choose laser at 488 nm, pinhole 40 μm, Master Grian at 660 V, Digital Offset at 0, and Digital Gain at 1.0. The image was taken with a 20× objective lens. Tile mode was used for image stitching. All steps comply with a protocol [39]. Root tissues from T3 generation seedlings were cultured on 1/2 MS medium without antibiotic for 8 days under long-day conditions. Primary roots from the shoot about 2 cm were tested. Petals were extracted from flowers at the top region of the stem, which were cultured on soil for 25 days. At least 5 biological replicates (roots or petals) for each genotype were used in the assays. 4.9. Flowering Time and Rosette Leaf Mass Measurements Flowering time was determined when the first flower was opened or reached to phrase 6.00, according to the method of Douglas C. Boyes [40]. At the same time, the number of rosette leaves was counted. Total rosette fresh mass of each genotype was measured by an analytical balance when experimental plants were cultured on soil for 15 days. Dry weight was measured after drying for 7 days at 70 °C. 4.10. Chlorophyll Fluorescence Measurement The 4th real rosette leaf of each genotype was used to conduct chlorophyll fluorescence measurement by DUAL-PAM/F (Walz, Effeltrich, Germany) when plants were cultured on soil for 15 days. All transgenic plants were placed in a dark environment for 20 min. Dual channel mode Fluo + P700 and SP-analysis was used for measuring. Each pulse was spaced 30 s apart for a total of five minutes. Values were derived from light-induced curves. The results were automatically output by the instrument. The measurement mode complied with the advice of the engineer.

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Abstract Liriodendron chinense (Hemsl.) Sarg. (Magnoliales: Magnoliaceae), valued for its medicinal properties and timber and as an ornamental plant, is now classified as an endangered species. Investigating how future climate-change scenarios might affect the potential geographic distribution of L. chinense will provide a crucial scientific basis for its protection and management strategies. The MaxEnt model was calibrated using the ENMeval optimization package, and then it was coupled with ArcGIS 10.8 to forecast the possible distribution areas of L. chinense in China, utilizing elevation data, bioclimatic factors, and human footprint as environmental variables. The results indicate: (1) The optimal model parameters were set as follows: FC = LQ, RM = 0.5, the MaxEnt model demonstrated high predictive accuracy and minimal overfitting; (2) The total suitable habitat area for the potential geographical distribution of L. chinense during the current period is estimated at 151.55 × 104 km2, predominantly located in central, eastern, and southwestern regions of China; (3) The minimum temperature of the coldest month (bio6), precipitation of the driest month (bio14), precipitation of the driest quarter (bio17), precipitation of the warmest quarter (bio18), elevation (alt), and human footprint (hf) are the main environmental variables determining the suitable habitat distribution of L. chinense; (4) During the period from 2041 to 2060, under the carbon emission scenarios of SSP126, SSP245, and SSP370, the suitable habitat for L. chinense shows varying degrees of increase compared to the current period. However, under the highest concentration scenario of SSP585, the suitable habitat area decreases to some extent; (5) The distribution of L. chinense is likely to move towards higher latitudes and elevations in the future due to changes in the climate. This research provides a comprehensive analysis of the potential impacts of climate change on L. chinense, offering valuable information for its protection and management under future climatic conditions. Keywords: climate change; ENMeval; Liriodendron chinense; MaxEnt; potential habitat

1. Introduction According to the Intergovernmental Panel on Climate Change’s Sixth Assessment Report (IPCC AR6), the global average surface temperature from 2011 to 2020 was 1.09 °C higher than that from 1850 to 1900 [1]. In terms of global warming, the rate of warming in China over the past century has slightly surpassed the global average [2]. Climate change is identified as a pivotal driver of accelerated biodiversity loss across both regional and global scales [3]. Changes in rainfall patterns and rising temperatures have impacted the health of American Populus forests and resulted in a decline in their population numbers [4]. The rising temperatures and shifts in precipitation patterns, as a consequence of climate change, extend beyond influencing plant growth and development; they are likely to profoundly affect the geographic distribution and population sizes of various species [5,6]. Plants are inclined to recalibrate their ecological distributions, notably migrating to higher latitudes or elevations in search of optimal survival conditions to evade the threat of extinction [7,8]. Meanwhile, human activities of varying scopes, modes, and intensities have shaped diverse land use types, directly impacting the distribution and diversity of plants [9]. Investigating the adaptive responses of plants to climate change, along with their potential habitat distributions under diverse carbon emission scenarios over varying time frames, enriches our comprehension of the dynamics driving shifts in plants’ viable habitats [10]. This knowledge empowers researchers and decision-makers to devise and implement more effective strategies for mitigation and preservation. Species Distribution Models (SDMs) serve as crucial tools for examining the impact of environmental changes on the suitable habitats of species [11,12]. Currently, models used to study the suitability of species distribution include the Bioclimatic Prediction System (BIOCLIM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gower Distance-Based Domain Mode (Domain), Environmental Distance (ED), Climatic Envelope (CE), Genetic Algorithm for Rule-set Prediction (GARP) and the Maximum Entropy Algorithm (MaxEnt) [13,14]. Wang et al. [15] compared the predictive accuracy of four SDMs—GARP, Bioclim, Domain, and MaxEnt—for predicting the potential habitat of Pseudolarix amabilis. The results indicated that MaxEnt had the highest predictive accuracy and provided the most objective predictions. Ashraf et al. [16] employed seven SDMs (MaxEnt, GARP, BIOCLIM, ANN, SVM, CE, and ED) to predict the geographical distribution changes in Olea europaea in Asia. The results showed that MaxEnt and SVM had better accuracy, and MaxEnt stands out for its user-friendly interface, minimal distortion, high stability, and exemplary simulation performance. Remarkably, it requires only a modest amount of data to deliver precise predictions, establishing it as a preferred choice among predictive models. At present, it ranks as one of the most widely utilized models for species distribution. The MaxEnt model, frequently utilized in conjunction with ArcGIS, plays a pivotal role in forecasting shifts in the habitats suitable for endangered species. Ye et al. [17] used MaxEnt and ArcGIS to study the impact of climate change on the distribution pattern of the endangered plant Semiliquidambar cathayensis after the last interglacial period. Similarly, Liu et al. [18] utilized MaxEnt and ArcGIS to forecast the global ecological suitability areas for the endangered Japanese species, Pyrus calleryana, under various climate conditions. Such research endeavors have significantly contributed to laying a scientific groundwork for the protection and conservation of endangered tree varieties. Liriodendron chinense (Hemsl.) Sarg. (Magnoliales: Magnoliaceae), a rare species within the Magnoliaceae family and the Liriodendron genus, is a Tertiary relic plant that holds an important position in the history of biological evolution. Due to its significant ornamental value, it is frequently used in current urban landscape and garden greening projects [19]. Additionally, L. chinense is valued for its high-quality timber and possesses notable medicinal properties [20]. In recent years, L. chinense has seen a significant decline in its natural populations, a situation exacerbated by human-induced habitat degradation and inherent reproductive biological constraints [21]. The International Union for Conservation of Nature (IUCN) underscores that global warming poses a threat to at least 10,967 species listed on the IUCN Red List of Threatened Species, elevating their likelihood of extinction [22]. Against this backdrop, L. chinense has been designated as a Near Threatened species on the IUCN Red List [23], signaling the urgency for conservation measures. Under the current and projected impacts of climate change, the distribution range and growth conditions of L. chinense could face significant challenges. Simulations predicting the suitable habitats of L. chinense are crucial for understanding future distribution patterns and identifying the key environmental variables affecting its distribution. Previous studies on the geographical distribution patterns of L. chinense has utilized the MaxEnt model with default settings [24,25], leading to overfitting and sampling bias, which directly impairs the transferability of species predictions. Moreover, these studies have relied on climate data from the CMIP5 model, and there is still a lack of clear understanding based on the latest CMIP6 model. Additionally, existing research lacks an analysis of the impact of human activities on the growth of L. chinense. As global temperatures continue to rise, L. chinense is predominantly cultivated in southern China, yet projections suggest a potential northward expansion of its distribution. Winters in northern China are markedly influenced by the cold northern currents, which are effectively blocked by the Qinling, Zhongtiao, and Dabie mountains. These geographical barriers establish the northern limit of this species distribution in southern China. It is hypothesized that with future increases in average annual temperatures and a diminished influence from northern cold currents, L. chinense may find suitable climatic conditions extending into some northern regions. This research leverages the ArcGIS software and an optimized MaxEnt model to address several key questions: (1) Investigating the distribution pattern of the potentially suitable areas for L. chinense under current climate conditions in China and classifying them into different suitability grades; (2) Identifying the primary environmental variables that limit its geographic distribution; (3) Forecasting the trends of potential geographical distribution shifts under future climate scenarios; (4) Mapping the trajectories of its centroid migration across different carbon emission scenarios. The findings from these analyses are intended to underpin the introduction and cultivation strategies for L. chinense, facilitating the expansion of its viable habitat range with a scientific foundation.
2. Materials and Methods This study follows the methodology of data acquisition, data processing, model optimization, model operation, species geographic distribution, suitable habitat change, and centroid analysis. The specific research workflow is presented in Figure 1. Forests 15 00988 g001 Figure 1. Study framework diagram. 2.1. Sources and Processing of Species Distribution Data The distribution data of L. chinense were obtained from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/, accessed on 21 August 2023) and the Chinese Virtual Herbarium (CVH, https://www.cvh.ac.cn/, accessed on 22 August 2023). The time period was limited to 1970–2020, when precise geographic coordinates were unavailable for occurrence records. We utilized Google Maps to ascertain longitude, latitude, and altitude information. Then, we removed duplicate and ambiguous data and confirmed that the coordinate information matched the distribution data. A total of 273 natural occurrence records were obtained across China. To mitigate sampling bias caused by the clustering effect and eliminate redundant data to prevent overfitting, the ENMTools v1.3 software was utilized for filtering distribution data. ENMTools can automatically match the grid size of environmental factors, eliminate redundant data within the same grid, and ensure that each grid retains only one valid distribution point [26]. Within identical grid cells (5 km× 5 km), only a single distribution point was retained, and the data were converted into .csv format for the development of the MaxEnt model. This process yielded a refined and accurate dataset comprising 87 distribution records (Figure 2). Forests 15 00988 g002 Figure 2. Map of distribution data of Liriodendron chinense. The pink dots represent the 87 distribution points of L. chinense within China, after removing instances of high spatial autocorrelation and duplicate records. 2.2. Collection and Processing of Environmental Variable Data As shown in Table 1, the environmental variables selected for this study encompass 19 climate data points, one elevation variable, and one human footprint. Bioclimatic and elevation data were sourced from the WorldClim database (https://worldclim.org/, accessed on 23 August 2023), with a spatial resolution of 2.5 min [27]. The human footprint (hf) data were obtained from the third edition of the Global Human Disturbance dataset (http://www.ciesin.columbia.edu/wild_areas, accessed on 23 August 2023) [28], hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University [28] (2016). This dataset includes comprehensive annual records from 1993 to 2009, capturing a range of human activities such as population density, railroads, highways, power infrastructure, cropland, and grazing. These data provide a detailed characterization of human activity disturbances, which is crucial for understanding how urbanization and infrastructure development could alter habitats and influence the geographical distribution of L. chinense. The data were weighted and standardized, resulting in a spatial resolution of 5 km. Administrative boundary data for China were sourced from the Resource Environment Science and Data Center (https://www.resdc.cn/, accessed on 24 August 2023). Table 1. Environmental variables used for MaxEnt model prediction.

This study utilized current bioclimatic data spanning from 1970 to 2000 and projected bioclimatic data for the 2050s (2041–2060). Compared to the BCC-CMS 1.1 m model (in terms of advantages over RCP scenarios), the BCC-CMS2-MR global circulation model (GCM) under the CMIP6 scenario, released by IPCC6, has demonstrated improved simulations in the evolution of global temperatures and the spatial distribution of annual average precipitation across China [29]. Consequently, we have chosen this model to forecast the suitable habitats for L. chinense in China. This model includes four carbon emission scenarios: SSP126, SSP245 and SSP370, and SSP585 [30]. SSP126 represents a low carbon emission pathway, envisioning a more environmentally friendly future that mitigates the impacts of global warming and climate change while also supporting sustainable economic and social development. SSP245 and SSP370 are medium emission reduction pathways, representing a relatively stable future that can lessen the impacts of global warming and climate change, though they may impose some limitations on economic and social factors. SSP585 is a high-emission pathway, depicting a more unstable and unsustainable future. This scenario could lead to more severe climate change and environmental challenges, adversely affecting economic and social development. For regional studies within China, a resolution of 2.5 min is generally considered sufficient [31]. In global or macro-ecological research, using an excessively fine resolution typically does not yield significant improvements in model performance [32]. All environmental variables were processed through ArcGIS 10.8 software, standardized to a 2.5 min resolution, with the coordinate system set to Albers. In species distribution modeling (SDM), the multicollinearity among environmental variables can lead to model overfitting, reducing the precision of prediction outcomes [32]. To address this, a Spearman rank correlation analysis was conducted to examine the interrelationships among variables, generating a correlation coefficient matrix using R 3.6.3, and variables with an absolute correlation coefficient value of less than 0.8 were retained [33,34]. Based on this, the contribution of 21 influencing factors was determined using the Jackknife test method in MaxEnt 3.4.4 software. Based on their levels of contribution, 13 variables (Figure 3, the variables enclosed in black dashed boxes) were ultimately selected for predicting the potential geographic distribution of L. chinense. Forests 15 00988 g003 Figure 3. Heat map of correlation between 21 environmental variables. 2.3. Model Optimization and Accuracy Evaluation To approximate a normal distribution for the likelihood of occurrence of L. chinense, 75% of the data were designated for model training, with the remaining data set aside for model testing, keeping other values at their defaults [35]. The regularization multiplier (RM) and feature classes (FCs) are key parameters affecting the predictive accuracy of the MaxEnt model [36]. The default settings in MaxEnt often lead to overfitting and are usually not the most optimal configuration. The ENMeval package in R version 4.2.3 was utilized to optimize and fine-tune the MaxEnt model [37]. The optimization process involved testing eight different values of the regularization multiplier (RM), ranging from 0.5 to 4 with a step of 0.5, and six types of feature combinations (Feature combination, FC)—L, LQ, LQH, H, LQHP, and LQHPT, where L stands for linear, H for hinge, Q for quadratic, P for product, and T for threshold. These were cross-combined in a comprehensive evaluation. The ENMeval package was employed to test the 48 resulting combinations, selecting the optimal parameter combination based on the lowest delta Akaike Information Criterion (Δ.AICc = 0). The filtered geographic distribution data and environmental variables were imported into the MaxEnt software for modeling calculations. The predictive performance of the model was evaluated using the receiver operating characteristic curve (ROC) [38]. In ROC analysis, the false positive rate (1-specificity) is plotted on the x-axis, and the true positive rate (sensitivity) is plotted on the y-axis, while the area under the curve (AUC value) is used to measure the accuracy of the predictions. An AUC value ranging between 0.70 and 0.80 indicates the predictions are fairly accurate; if the AUC value falls between 0.80 and 0.90, the predictions are considered quite accurate; and an AUC value exceeding 0.90 signifies that the predictions are highly accurate [39,40]. Furthermore, True Skill Statistic (TSS = Sensitivity + Specificity − 1) is a straightforward and intuitive statistic used to evaluate the performance of species distribution models [41]. The range of TSS spans from −1 to 1 and is categorized as follows: poor (−1 to 0.4), fair (0.4 to 0.5), good (0.5 to 0.7), very good (0.7 to 0.85), excellent (0.85 to 0.9), and almost perfect (0.9 to 1) [42]. Therefore, in this study, both AUC and TSS are employed to demonstrate the predictive performance of the MaxEnt model. 2.4. Suitability Classification and Analysis of Dominant Environmental Variables Result files generated by MaxEnt software (in .asc format) were imported into ArcGIS. Utilizing the Reclassify command within the Spatial Analyst Tools of ArcGIS and adopting Jenks’ natural breaks method, the potential suitable habitats for L. chinense across China were categorized into four distinct levels [18]: non-suitable areas (0 ≤ p ≤ 0.1), low-suitability areas (0.1 < p ≤ 0.2), moderate suitability areas (0.2 < p ≤ 0.4), and high-suitability areas (p ≥ 0.4) [43]. The dominant environmental variables influencing the suitable distribution of L. chinense were identified by integrating the Jackknife test, percent contribution, and permutation importance analysis [44]. The influence thresholds of these dominant environmental variables were determined by analyzing the response curves to various environmental factors, revealing the potential distribution probabilities. 2.5. Centroid Analysis of Suitable Areas Using the SDM toolbox in ArcGIS, we computed the trends in changes in current and future suitable areas and compared the quality centers of these suitable areas. The coordinates change and migration distance of the quality centers of L. chinense were described using ArcGIS [45]. 3. Results 3.1. Optimization and Accuracy Evaluation of the MaxEnt Model As illustrated in Figure 4, the AUCtrain and AUCtest values for the MaxEnt models developed under various climate scenarios all exceed 0.9, satisfying the criteria for “high accuracy” [46]. Additionally, the TSS values are greater than 0.8, classifying them within the “very good” category [47]. These metrics indicate that the model effectively captures the distribution patterns of Liriodendron chinense, ensuring a high level of confidence in its predictive accuracy. Forests 15 00988 g004 Figure 4. MaxEnt model accuracy evaluation. As shown in Table 2, the default feature combination (FC) of the MaxEnt model shows FC = LQHPT, RM = 1, delta.AICc = 153.929412. After optimization using the ENMeval package, the feature combination of the MaxEnt model was adjusted to FC = LQ, RM = 0.5, delta.AICc = 0, indicating that the model with these parameters is the most optimal. Table 2. Evaluation metrics of MaxEnt model generated by ENMeval.

3.2. Dominant Environmental Variables Influencing the Distribution of L. chinense As indicated in Table 3, among the 13 environmental variables used in the MaxEnt model to predict the suitable distribution areas for L. chinense, the top three contributing factors are precipitation of the driest month (bio14, 34.9%), precipitation of the driest quarter (bio17, 21%), and human footprint (hf, 10.3%), with a cumulative contribution rate of 66.2%. The top three environmental variables by permutation importance are precipitation of the warmest quarter (bio18, 23.8%), minimum temperature of the coldest month (bio6, 21.8%), and elevation (alt, 10.1%). Table 3. Environmental variables and their contributions and suitable value ranges.

The Jackknife test results (Figure 5) reveal that when only a single environmental variable is used, the three variables that have the greatest impact on the regularized training gain are human footprint (hf), elevation (alt), and precipitation of the warmest quarter (bio18), indicating that these variables contain unique information not present in others. Taken together, the dominant environmental variables influencing the contemporary geographic distribution of L. chinense in China are temperature variables (minimum temperature of the coldest month), precipitation variables (precipitation of the driest month, precipitation of the driest quarter, and precipitation of the warmest quarter), elevation, and human footprint. It is generally considered that the environmental variable ranges corresponding to a logistic output greater than 0.5 are the thresholds for species-suitable distribution. The response curves for the dominant environmental variables affecting the distribution of L. chinense are shown in Figure 6, with other environmental variable ranges suitable for the growth of L. chinense presented in Table 3. Forests 15 00988 g005 Figure 5. The Jackknife test result of environmental factor for L. chinense. Forests 15 00988 g006aForests 15 00988 g006b Figure 6. Response curve of 6 main environmental variables. (a) Response of bio14; (b) Response of bio17; (c) Response of bio18; (d) Response of bio6; (e) Response of alt; (f) Response of hf. Red line, the mean response of the 10 replicate Maxent runs. Blue part, the mean +/− one standard deviation. 3.3. Potential Suitable Distribution Areas of L. chinense under Current Climate Conditions Climate scenarios typically refer to projections about future climate conditions based on various assumptions about how factors such as greenhouse gas emissions, land use, and other drivers of climate change might evolve. These scenarios are used to model and analyze potential future climates to understand how changes in climate could affect the Earth’s systems, ecosystems, and human societies. They play a critical role in climate research. Utilizing the MaxEnt model alongside ArcGIS, the potential suitable habitats for L. chinense under the prevailing climate conditions were mapped (Figure 7). Among the refined set of 87 valid distribution records of L. chinense, the highest logistic probability location is Lushan, Jiujiang City, Jiangxi Province (0.91), and the lowest is in Gaolan County, Lanzhou City (0.01), with an average logistic probability of 0.52. According to Table 4, L. chinense’s total suitable habitat area under current climate conditions spans approximately 151.55 × 104 km2, constituting 15.73% of China’s landmass. This habitat is predominantly located in the central, southeastern, and some southwestern parts of China, extending from Shaanxi and Anhui provinces southwards, west to Sichuan and Yunnan, and south to Hainan, including suitable cultivation zones in Taiwan Province. The high-suitability areas, covering about 35.52 × 104 km2, 3.70% of China’s total area, are chiefly found in Chongqing, the southwestern regions of Hubei Province, and the northwestern parts of Hunan Province. Forests 15 00988 g007 Figure 7. Potential geographical distribution of L. chinense under current climate conditions. Table 4. Suitable areas for L. chinense under different climate-change scenarios (×104 km2).

3.4. Forecasting the Impact of Future Climate Change on the Potential Geographic Distribution of L. chinense The MaxEnt model was utilized to predict changes in the potential suitable distribution of L. chinense in China under four emission pathways for 2050: SSP126, SSP245, SSP370, and SSP585, as depicted in future climate scenarios (Figure 8). Forests 15 00988 g008 Figure 8. Potential geographical distribution of L. chinense under future climate scenarios. As presented in Table 4 and Figure 8, within the context of the SSP126 carbon emission scenario by 2050, the habitat area highly suitable for L. chinense is projected to expand by 15.82 × 104 km2 beyond current climatic conditions. The area of medium-suitable habitat is expected to increase by 10.79 × 104 km2 and that of low-suitability habitat by 7.49 × 104 km2, cumulatively enhancing the overall suitable habitat by 34.10 × 104 km2. In scenario SSP245 for 2050, the expansion in highly suitable habitat areas for L. chinense is forecasted at 19.83 × 104 km2, with medium-suitability areas growing by 26.85 × 104 km2 and low-suitability areas by 19.02 × 104 km2, totaling a significant habitat increase of 65.69 × 104 km2. Under the SSP370 emission trajectory for 2050, the increase in highly suitable habitats is anticipated to be an impressive 50.51 × 104 km2, with medium and low-suitability habitats expanding by 52.90 × 104 km2 and 31.92 × 104 km2, respectively, aggregating a substantial total habitat growth of 135.33 × 104 km2. In the case of the SSP585 emission scenario for the same year, the highly suitable habitats are expected to grow by 33.52 × 104 km2, with medium and low-suitability habitats seeing increases of 48.14 × 104 km2 and 32.36 × 104 km2, respectively, leading to an overall habitat expansion of 114.02 × 104 km2. As illustrated in Figure 9, a comparative analysis against current climatic conditions reveals that for 2050, the areas designated as suitable habitats for L. chinense under the SSP126, SSP245, SSP370, and SSP585 climate scenarios demonstrate a trend of initial expansion followed by a subsequent contraction. Specifically, the SSP370 scenario indicates the most pronounced augmentation in habitat suitability, with both high and medium-suitable regions experiencing an approximate 30% increase in area. This scenario culminates in the zenith of overall habitat suitability. Conversely, under the SSP585 scenario, characterized by the highest concentration of emissions, there is a reduction in habitat suitability areas. Forests 15 00988 g009 Figure 9. Proportion of suitable habitats for L. chinense under different climate-change scenarios. 3.5. Shift in the Centroid of L. chinense’s Suitable Distribution Area The geometric centroid results of the suitable habitats for L. chinense under four climate scenarios indicate that the centroid coordinates are located within Wufeng Tujia Autonomous County, Zigui County, and Fang County in Hubei Province, as well as Shimen County in Hunan Province, which borders Hubei. As illustrated in Table 5 and Figure 10, over time, the centroid of L. chinense’s suitable distribution areas under all four carbon emission scenarios tends to shift gradually towards the north and to higher elevation areas. Specifically, under the SSP370 carbon emission scenario, the centroid shifts the furthest distance from the current position by 446.49 km, and under the SSP585 climate scenario, it reaches the highest elevation, at 1165 m. Forests 15 00988 g010 Figure 10. Centroid migration distance of L. chinense under different climate scenarios. Table 5. Location of the centroid shift and migration distance of L. chinense under different climate scenarios.

4. Discussion 4.1. The Influence of Environmental Variables on the Modern Potential Geographic Distribution of L. chinense Plant survival is intimately connected with environmental conditions, with climate—encompassing both temperature and precipitation—serving as the critical determinant of plant regeneration and geographic distribution [48]. The results demonstrate that, under the prevailing climate conditions, L. chinense predominantly thrives in the transitional regions between China’s subtropical monsoon and temperate climates. The gentle and humid climate of these regions promotes the growth of L. chinense, reflecting its preference for moisture and capacity to withstand high temperatures. In the study on the distribution patterns of endemic plant groups, López-Pujol et al. [49] discovered that many relict plants, including L. chinense, have a preference for surviving in mild climatic conditions. Our research findings align with their observations. Among the 13 environmental variables selected to influence the geographic distribution of L. chinense, precipitation of driest month(bio14) stands out as having the most significant impact, aligning with the findings of Cao et al. [24]. The overall suitability probability for L. chinense increases with greater precipitation of driest month (bio14) and quarter(bio17), consistent with the research by Elliott et al. [50]., which suggested that drought significantly affects the growth of L. chinense. While precipitation variables play a primary role in influencing the potential geographic distribution of L. chinense, changes in temperature also represent a critical factor that cannot be overlooked. When considering the impact of climate change on vegetation distribution, the effects of temperature fluctuations on plant latitudinal migration are significant. Particularly against the backdrop of intensifying global greenhouse effects, regions limited by heat will gradually expand if natural precipitation factors are not considered, implying shifts in plant growth areas. Low temperatures significantly reduce the fluidity of cell membrane lipids, which can restrict the function of membrane proteins [51]. This restriction impacts the controlled import and export of substances, potentially leading to metabolic disorders in plants [52]. Although L. chinense has some degree of cold tolerance, the lowest temperature of the coldest month still significantly impacts its growth. Normal growth may be affected when the minimum temperature(bio6) drops below −1.38 °C or rises above 6.7 °C, closely aligning with the findings of Qiu Haojie et al. [25]. Within the multitude of factors influencing the habitat of L. chinense, elevation and human footprint emerge as significant, contributing 9.0% and 10.3%, respectively, to its distribution. Human footprint, a pivotal determinant of plant distribution dynamics, exerts both facilitative [53] and inhibitory [9] effects on the distributional shifts across different plant species. As human societies have evolved, diverse human actions—including land use alterations, deforestation, and urban expansion—have resulted in varied land use types. The study’s analysis reveals a trend in response to human footprint: an initial positive influence on the growth of L. chinense is observed, which diminishes and turns negative with increasing disturbance. This suggests an optimal threshold of human interaction beyond which the environmental stress adversely impacts the species’ survival. Furthermore, the response curve for elevation suggests that L. chinense is more suited to grow in higher elevation areas. Consequently, the interplay of these factors, coupled with climate variables, orchestrates a complex and fragmented distribution pattern of L. chinense’s potential geographic spread. 4.2. Changes in the Geographic Distribution of L. chinense under Different Climate-Change Scenarios Climate change has altered the structure and function of terrestrial ecosystems, therefore changing the habitats and geographic distribution of species [54,55]. Using the MaxEnt model, predictions were made regarding changes in the potential geographic distribution of L. chinense in China under future climate-change scenarios (Figure 11). The predictions indicate that by 2050, compared to current climate conditions, the range of suitable habitats for L. chinense will show characteristics of continuous expansion under low and medium-concentration pathways while slightly contracting under high-concentration pathways. The expansion areas are mainly distributed in higher latitude provinces such as Shaanxi, Henan, Shanxi, Shandong, Jiangsu, and Anhui, while suitability decreases or disappears in potential geographic distribution areas located in lower latitude regions. Guan et al. [52] suggest that global warming may facilitate the growth of certain species [56], a finding that aligns with this study’s indication of an overall increase in suitable habitat areas for L. chinense under future climate conditions. This could be related to L. chinense’s leaf heat resistance and the plant’s ability to tolerate mild drought and high temperatures [57]. Forests 15 00988 g011 Figure 11. Changes in potential geographical distribution of L. chinense under climate-change scenarios in the future. However, L. chinense’s suitable ranges for temperature and precipitation are within specific limits, suggesting that continued high concentrations of greenhouse gases leading to climate change could potentially reduce or eliminate its suitable habitats, ultimately threatening the species with extinction. This highlights that increased precipitation under certain carbon emission pathways could mitigate or resolve the limitations precipitation places on the distribution of L. chinense. In contrast, the continuously rising temperatures under high-emission pathways could hinder the healthy growth of L. chinense, leading to a potential loss of geographic distribution in lower latitude areas. This aligns with Liu et al.’s [58] findings on the potential suitable habitats for Triadica sebifera in China, demonstrating a trend where the habitats slightly increase before declining as carbon emissions rise. Jiang et al. (2008) [59] reported that China’s climate in the 21st century is likely to be warmer and wetter. Warming by the end of this century is expected to range between 1.6 °C and 5 °C, with an annual precipitation increase of 1.5% to 20%. Global warming may alter the distribution patterns of soil microbial activity, therefore accelerating the decomposition of soil organic matter [60]. These factors contribute to providing suitable living conditions for plant growth. These studies collectively suggest that future climate change aligns with the species characteristics of Liriodendron chinense and its preference for warm and moist conditions, and the potentially suitable area for Liriodendron chinense is expected to expand further in the future. Climate simulation results for the modern era indicate a predominant distribution of L. chinense in areas located in the southern part of China. In contrast, the northern regions of China, which are primarily characterized by temperate continental and monsoon climates, exhibit lower annual precipitation and cold, dry winters [61]. These climatic conditions are less conducive to the growth of L. chinense, resulting in lower habitat suitability within these areas. Intriguingly, despite the absence of natural geographic distribution data for Taiwan Province in existing records, climate simulations for the current period have identified the climatic conditions of Taiwan Province as falling within the suitable environmental range for L. chinense. This discovery opens up future prospects for the introduction and cultivation of the species in Taiwan, underscoring the potential for expanding its distribution in response to favorable climatic conditions. Compared to its potential geographic distribution under current climate conditions, the suitable habitats for L. chinense in 2050, under four emission scenarios, exhibit a trend of shifting towards higher latitudes and elevations. This movement aligns with the findings of Zhang et al. [62], who observed a similar trend towards higher elevations in the potential geographic distribution of Populus euphratica under future climate-change scenarios. In addition, Prevey et al. [63] predicted that in the future, the distribution of Vaccinium membranaceum will decrease in the southern low-elevation areas and expand in the high-elevation and high-latitude areas. Consequently, the cultivation and maintenance of L. chinense will continue to necessitate sustained human support, both now and in the future, highlighting the importance of adaptive management practices in response to changing climatic conditions. The results of centroids suggest that the diversity center of L. chinense’s natural distribution is within Hubei Province. This is consistent with the findings of Qiu et al. [25], who identified Hubei Province as a major potential distribution area for L. chinense. Therefore, Hubei Province could be prioritized for the relocation and conservation efforts of L. chinense in the future. China is in the East Asian monsoon region, adjacent to the Pacific Ocean. Studies have indicated that due to future global climate changes, temperatures in China’s coastal areas are expected to rise significantly. Consequently, vegetation is projected to migrate northward [61]. The centroids show a trend of moving northward and toward higher elevations, aligning with previous research findings that centroids migrate with climate warming [64]. 4.3. Methodological Limitation (1) Data collection: This study only collected and analyzed distribution data within China. Hence, the environmental factor response curves may not fully reflect the response of L. chinense to environmental changes. Moreover, the species distribution data covers the years 1970–2020, which does not completely synchronize with the climate data spanning from 1970 to 2000. This temporal mismatch is particularly relevant given that L. chinense, as a woody plant, exhibits a longer growth cycle, necessitating a broader time range to gather a comprehensive dataset. Although such temporal discrepancies are common in ecological research and generally do not significantly alter the analytical outcomes—especially in studies focused on long-term climatic effects [65]—they could potentially limit the precision of our findings. For future studies, expanding the data collection to include global distribution data of L. chinense would enhance our understanding of its responses to diverse environmental conditions. Additionally, ensuring tighter temporal alignment between species distribution and climate data will improve the accuracy of our response curve analyses and make our conclusions more robust. (2) Temporal scope and forecasting limits: The future of climate change holds many uncertainties, and this study only utilizes the BCC-CMS2-MR GCM to simulate the potentially suitable habitats for L. chinense, which increases the instability of the MaxEnt model’s predictions and leads to certain errors in geographic distribution and area occurrence. This can affect decision-makers’ ability to formulate ecological protection measures. Previous studies have demonstrated that employing multiple GCMs significantly diminishes uncertainties and errors in predictions [66,67]. Future research on predicting the potential suitable habitats for L. chinense should also adopt this approach. Furthermore, our study only utilized climate scenario data for 2050, which may not adequately reflect changes in the suitable habitat of L. chinense over different future time periods. While we acknowledge that there are high-quality climate datasets available for nearer periods, such as 2020–2040, our choice was guided by the need to provide insights into future climatic conditions that match the time frames most relevant to policy and decision-making processes. Using the 2041–2060 period provides a balance between near-term forecasting and long-term strategic planning. To more accurately predict the potential geographic distribution changes in L. chinense, it is recommended that future research employs dynamic analysis and comparison at multiple time points. (3) Consideration of ecological factors: In predicting the potential suitable areas for L. chinense, this study primarily focused on factors such as climate, altitude, and human footprint without fully considering biological factors like interspecies interactions. Given the existence of interactions and dependencies between species [68], these complex ecological relationships could impact the actual climate adaptability and survival conditions of species [69]. Therefore, the predicted potentially suitable areas might deviate from actual conditions. Future research should explore a wider range of ecological factors, including soils, land cover, and interspecies interactions, in greater detail to enhance the accuracy of predictions regarding the potentially suitable areas for L. chinense. Despite the aforementioned limitations, this study is the first time to employ the ENMeval package to optimize the MaxEnt model to predict with high reliability the potentially suitable areas for L. chinense, providing an important scientific basis for its conservation and introduction. There are many powerful methods based on machine learning and statistical data, such as information gain, the Gini index, random forest, relief, ANOVA, and Chi-square, which have shown good performance in optimizing MaxEnt models [70]. It would be worthwhile to explore the application of these methods for model optimization and parameter tuning in future studies. We will address these limitations with adjustments and improvements, further refining the future study of the potential distribution of L. chinense to more accurately guide its conservation and introduction efforts.
5. Conclusions This research utilizes the MaxEnt model, enhanced by the ENMeval package, to forecast the potential habitats and distribution patterns of the endangered species, L. chinense, both in the current periods and under future 2050s climate scenarios, across four carbon emission pathways. The study yields the following conclusions: (1) Under current climate conditions, the potential suitable habitat area for L. chinense spans 147.55 × 104 km2, accounting for 15.01% of China’s land area. It is primarily distributed in central, southern, and southwestern China, with cultivation also present in Taiwan Province. (2) Precipitation in the driest month, precipitation in the driest quarter, precipitation in the warmest quarter, minimum temperature of the coldest month, altitude, and human footprint are the dominant factors influencing the suitable habitat distribution of L. chinense. Among these, precipitation in the driest month (bio14) is the most critical determinant of the suitable environment for L. chinense. (3) Under future climate scenarios, with increasing carbon concentrations, the distribution range of L. chinense is expected to initially expand and then contract, reaching its maximum expansion under the SSP370 emission scenario. (4) In the future 2050s climate scenarios, the centroid of L. chinense’s distribution is predicted to shift from lower latitudes to higher latitudes in the north and from lower to higher elevations. Author Contributions

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South Korean forests need hardwood tree species that can produce timber, as global warming progresses and the habitats of conifers dwindle. For the past 30 years, exotic yellow poplar (Liriodendron tulipifera) has been planted to replace some of the pine-dominated forests, as there is a lack of native hardwood tree species that produce large and good quality timber. However, yellow poplar growth has varied among planting sites across the country. We studied how environmental factors affect the growth of 49 stands of yellow poplar trees, with 945 dominant trees across 129 plots. To identify the optimal conditions for yellow poplar growth, we assessed 28 environmental variables, including geographic, climatic, topographic, and soil properties, for their correlation with volume growth. We estimated the optimal conditions for yellow poplar growth by averaging the values of the variables for the top five performing stands. To calculate the relative distance of any stand from the optimal conditions, we divided the difference between the stand’s values for the environmental variables and the optimal conditions by the standard deviation of those variables. We then calculated Spearman rank correlation coefficients between these distances and volume growth rankings. Wind exposure (WE), growing season temperature (GT), Latitude (LN), soil phosphorus pentoxide (P2O5) content, low extreme temperature during January and February (LT), and spring humidity (SH) were the most important environmental factors governing growing sites for yellow poplar in Korea, with WE being the most critical. Some variables showed synergistic effects and correlated slightly more strongly with volume growth when combined with the WE variable. Our study provides crucial insights for optimizing plantation management and site selection in non-native ranges, enhancing overall success in establishing yellow poplar plantations in South Korea. Keywords: yellow poplar (Liriodendron tulipifera); plantation; environmental factors; growth; adaptation

1. Introduction Yellow poplar (Liriodendron tulipifera), native to North America, has been planted in various regions of South Korea over the past fifty years [1]. It has shown excellent adaptability and successful growth in several test regions and has been widely planted across Korea to meet the rising demand for timber. However, forestation efforts with the species were then halted due to poor growth in some areas. Nevertheless, yellow poplar has recently regained attention as a promising forestation species in Korea, due to its high carbon sequestration potential and resilience to climate change. This resurgence is driven by a government initiative to achieve carbon neutrality by 2050. Historically, Korea has relied heavily on conifers such as red pine and Korean pine for forestation. However, these conifers have several drawbacks, including slow growth, susceptibility to wildfires, and a shrinking natural habitat range due to global warming [2,3]. As a result, there is a growing demand for fast-growing hardwood species like yellow poplar to replace conifers. Yellow poplar is well-suited for Korean forests, as it is tolerant of a wide range of site conditions and can grow quickly [4]. However, despite high expectations, some yellow poplar plantations in Korea have performed poorly. The poor performance of some yellow poplar plantations may be due to unsuitable site conditions, such as climate, topography, or soil properties [5]. The Korean climate is similar to the northern edge of yellow poplar’s native range, with a temperate climate and distinct seasons. However, there are subtle differences, such as Korea’s dry winter and spring seasons [6,7]. Despite its relatively small size, the Korean Peninsula has a diverse climate due to variations in altitude, topography, and proximity to the surrounding seas. Moisture-loving trees like yellow poplar tend to thrive on east and north slopes [8,9]. Other factors that influence tree growth include soil depth, drainage, weeding, and fertility [10]. Understanding the influence of environmental factors on yellow poplar growth in Korean plantations is crucial for future large-scale expansion. However, given the multitude of interacting factors that influence growth, drawing conclusions based on individual factors alone is challenging. This study aims to identify the environmental factors that influence yellow poplar growth in Korea, to inform large-scale reforestation efforts for this economically important species. We hypothesized that the failure of yellow poplar plantations is caused by unsuitable climate and unfavorable local topography. To test this hypothesis, we examined the volume growth of trees of the same age under different environmental conditions and analyzed the correlations between volume growth and multiple environmental variables.
2. Materials and Methods 2.1. Description of the Study Areas Forty-nine yellow poplar stands established between 2001 and 2010 were selected throughout the country for this study. The seeds were introduced from a seed orchard in Tennessee, USA and raised in Korean forest nurseries before planting. The 49 yellow poplar stands were located throughout Korea, between 34.5528° N and 38.2885° N latitude and 126.5272° E and 129.4114° E longitude. The stands ranged in altitude from 40 to 873 m above sea level. A total of 129 plots, each measuring 10 by 20 m, were established in the stands. In large stands, three plots representing good, fair, and poor growth were established, at least 20 m apart. In small stands, a single plot representing fair growth was established. The following information was measured and recorded at each plot: elevation, latitude, longitude, drainage, topographic (slope) position, slope steepness, wind exposure extent, azimuthal aspect, and soil depth of the plots. This information was obtained through web GPS application programs, including My Elevation (RDH software 1.63, Windsor, CT, USA), Simple Inclinometer (Syleos Apps 1.70, https://androidapps-9f44e.web.app, accessed on 13 April 2021), and Compass (Melon software 1.5.0, https://play.google.com/store/apps/details?id=app.melon.icompass&hl=en&gl=US&pli=1, accessed on 13 April 2021). Effective soil depth and total soil depth were determined by digging a hole at the center of each plot and measuring the maximum depth to which the roots had penetrated (effective soil depth) and the distance to the bedrock (total soil depth). Soil pH, organic matter, total nitrogen, phosphate (P2O5), and exchangeable ions (potassium, calcium, and magnesium) were determined from soil samples taken from a depth of 5–10 cm below the organic layer in each plot. 2.2. Climate of the Study Area Climatic data of the past 10-year average from the 37 closest stations were extracted from the Open Met Data Portal in the National Climate Data Center (https://data.kma.go.kr/stcs/grnd/grndTaList.do?pgmNo=70, accessed on 3 June 2022). The highest and annual mean temperature of each stand was measured on July–August (the highest) and calculated by subtracting 0.65 °C for each 100 m difference in the altitude of the weather station nearby from that of the stand. In winter, the lowest temperature in January–February was calculated by subtracting 0.98 °C for each 100 m increase in altitude followed by the meteorology3 (http://www.meteo.psu.edu/wjs1/Meteo3/Html/stability.htm, accessed on 20 June 2023). Humidity was calculated to decrease by 4% for each 1 km increase in altitude [11]. 2.3. Volume Growth of Stands Stand performance data, including height and diameter at breast height (DBH), and survival rate, were collected from the plots. For each plot, five dominant trees were measured and recorded. Individual tree volume index was estimated by multiplying tree height by basal area. Mean annual volume growth was computed by dividing the volume by the tree’s age. However, growth patterns vary depending on species and environment, with younger trees typically growing faster than older trees [5,12]. To compare the growth of different stands, the mean annual increment index (MAI by HD2) was calculated at the forest stand (or survey plot) level, based on individual tree heights and DBH. Since many stands were established in different years, it was necessary to use the volume at the same age for comparative purposes. To adjust for this, growth rates based on age within the same harvesting position in the yield table were used to correct and calculate specific correction coefficients for each detailed age group, which were differentially applied within each interval [5,13] (Table 1). Table 1. Spearman correlation coefficients (r) of volume growth with altitude, latitude and longitude.

2.4. Environmental Distance of Each Stand from the Optimal Conditions To compare the climatic conditions of the yellow poplar stands to the optimal conditions for yellow poplar growth, we collected climate data from the three U.S. states (Tennessee, Kentucky, and West Virginia) where yellow poplar grows best. These climatic data were sourced from various authorities, including the National Oceanic and Atmospheric Administration (NOAA), state governments, and environmental agencies, and were used for analysis https://semspub.epa.gov/work/01/554362.pdf, http://weather.uky.edu/panevap.html, https://www.in.gov/dnr/water/files/pg24-57.pdf, accessed on 20 June 2023). We also selected four climate variables that were highly correlated with yellow poplar growth (r > 0.3, p < 0.05): annual mean temperature (AT), low extreme temperature during January and February (LT), growing season temperature (GT), and spring humidity (SH) by Pearson’s coefficient. To calculate the distance of each stand from the optimal environmental conditions, we used the following formula to standardize each variable: di = |(x − xi)|/SD (1) where x is the optimal environmental value for yellow poplar and xi (i = 1, …n) is the environmental value for the stand. SD is a standard deviation. The distance of a stand from the optimal environmental conditions was calculated by summing up the distances of the four climate variables and dividing by four as shown in Table 9. D = ∑di = ∑|(x − xi)|/SD × n − 1 (2) where D is the total distance, d is the relative distance of a stand, SD is the standard deviation, and n is the number of variables. We also calculated topographic and soil chemical distances for the environmental variables that were highly correlated with yellow poplar growth (r > 0.3, p < 0.05) by Pearson’s coefficient. We then calculated Spearman correlation coefficients between these distances and volume growth rankings. 3. Results 3.1. Overall Growth Figure 1 shows the mean annual volume growth of yellow poplar in the stands. The volume growth varied widely, from 0.0004 to 0.1175 m3, with an average of 0.0437 m3. Despite the similarity in climate, the volume growth varied between regions. The most favorable stands were located in the southernmost region, which has higher temperatures and precipitation. Interestingly, some stands in the northern region also exhibited robust growth, despite cooler temperatures than the native habitat. Conversely, weaker growth was generally observed in the eastern half of the country, even though favorable environmental factors were expected. There were a few exceptions, such as stands with lower precipitation levels. Forests 14 02003 g001 Figure 1. Distribution of sample stands. Latitude and longitude ranged from 34.5528 Nº to 38.2885 Nº and 126.5272 E° to 129.4114 E°, respectively. Distribution of mean volume growth (m3) of yellow poplar trees in the stands is shown at age 20 across the country. 3.2. Effects of Latitude, Longitude, and Altitude on Volume Growth of Yellow Poplar We analyzed the volume growth of yellow poplar across 129 plots, considering the variations in climate factors associated with latitude, longitude (LN), and altitude. The distribution of latitude revealed 15 plots below 35° N, 28 plots between 35° N and 36° N, 41 plots between 36° N and 37° N, 57 plots between 37° N and 38° N, and nine plots above 38° N. Spearman correlation coefficients were calculated due to non-normal data distribution. The correlation between latitude and volume growth was statistically not significant (r = −0.14, p = 0.327). However, stands at lower latitudes, especially in the southernmost regions, consistently showed better volume growth than those at higher latitudes. This is likely because the climatic conditions in the southernmost region of Korea more closely resemble the optimal growth conditions found in yellow poplar’s native habitat. The longitudinal range of yellow poplar plantations in Korea is less than 3 degrees, from 126.5272° E to 129.4114° E. LN showed a moderately negative association with volume growth (r = −0.34, p = 0.016, Table 2). While the correlation is not strong, it is worth noting that the cooler temperatures and reduced precipitation in the higher mountainous regions of the eastern peninsula may potentially have a negative impact on yellow poplar growth. Table 2. Spearman’s correlation coefficients (r) between temperature variables and Volume growth.

For altitude, the data encompassed 33 plots between 40 and 100 m, 49 plots between 201 m and 300 m, 17 plots from 301 to 400 m, and six plots above 400 m (Figure 2). Volume growth decreased with increasing altitude. Plots below 100 m showed the best growth, while plots above 400 m showed the poorest growth. A slight negative association was found between altitude and volume growth (r = −0.24, p < 0.097). Overall, the results indicate that yellow poplar growth in Korea declines with increasing latitude, longitude, and altitude. Forests 14 02003 g002 Figure 2. Changes in volume growth of yellow poplar along the altitude (left) and latitude (right). 3.3. Effects of Climatic Variables of the Stands on Volume Growth of Yellow Poplar Four temperature variables were analyzed: annual mean temperature (AT), growing season temperature (GT), mean July-August high temperature (HT), and mean January–February low temperature (LT). AT ranged from 6.44 °C to 13.78 °C, with an average of 11.52 °C. GT ranged from 12.36 °C to 18.74 °C, with an average of 17.49 °C. HT ranged from 29.69 °C to 36.61 °C, with an average of 34.09 °C. LT ranged from −20.09 °C to −8.65 °C, with an average of −13.67 °C. Spearman rank correlation coefficients were calculated between standardized volume growth and standardized temperature variables. As shown in Table 3 and Figure 3, three temperature variables showed moderate correlations with volume growth: AT (r = 0.41, p < 0.0010, GT (r = 0.38, p < 0.001), and LT (r = −0.32, p = 0.001). HT did not show a significant correlation with volume growth. Forests 14 02003 g003 Figure 3. Volume growth of yellow poplar by mean annual (left), and growing season (right) temperatures. Table 3. Spearman rank correlation coefficients (r) between precipitation variables and volume growth of yellow.

Four precipitation variables were analyzed: annual precipitation (AP), growing season precipitation (GP), winter precipitation (WP), and spring precipitation (SP). AP ranged from 1011 to 1518 mm, with an average of 1256 mm. GP ranged from 911 to 1374 mm, with an average of 1114 mm. WP ranged from 85 to 224 mm, with an average of 142 mm, and SP ranged from 60 to 129 mm, with an average of 78 mm per month. Spearman’s rank correlation between volume growth and AP, GP, and WP was weak. SP showed no significant correlation with volume growth (Table 4). However, volume growth was notably higher for precipitation levels over 90 mm (Figure 4). Forests 14 02003 g004 Figure 4. Volume growth of yellow poplar by spring precipitation. Table 4. Spearman rank correlation coefficients (r) between humidity variables and volume growth of yellow poplar.

Four relative humidity variables were analyzed: annual humidity (AH), growing season humidity (GH), winter humidity (WH), and spring humidity (SH). AH ranged from 62% to 77%, with an average of 69%. GH varied from 63% to 78%, with an average of 71%. WH ranged from 51% to 74%, with an average of 65%, while SH ranged from 54% to 72%, with an average of 61%. Spearman rank correlation coefficients between volume growth and AH, GH, WH, and SH were weak, ranging from 0.06 to 0.27 (Table 4). Effective water content, defined as the difference between total precipitation and actual evaporation, was also analyzed [14]. Yearly effective water content ranged from −339 mm to 497 mm, with a mean of −8.8 mm. For the growing season, it ranged from −321 mm to 239 mm, with a mean of −46 mm. Pearson’s correlation coefficients between volume growth and annual, and growing season effective water content were weak, at 0.23 and 0.24, respectively. 3.4. Climatic Distance of Yellow Poplar Stands from the Optimal Growing Conditions To assess the impact of climatic conditions on yellow poplar stands, Spearman correlation coefficients were calculated between various climate variables and volume growth rankings. AT, LT, GT, and SH showed significant correlations (p < 0.05) with coefficients higher than 0.3 (Table 5). Optimal conditions were determined based on the maximum values (MAX) of these variables. The relative distance of climate variables from the optimal conditions for each stand was calculated as described in the Materials and Methods section. Table 5. Ranges of some climate variables with Spearman correlation coefficients higher than 0.3.

Most climate variables were not normally distributed, so Spearman rank correlation coefficients (r) were used to assess the relationship between volume growth rankings and the total climatic distances of the stands. The combination of AT, LT, GT, and SH showed a moderate correlation (r = −0.43, p = 0.002) with volume growth. To comprehensively investigate potential correlations, over 120 combinations of 2, 3, 4, 5, and 6 climate variables were tested for their association with volume growth. However, no other combinations produced higher correlation coefficients. These findings suggest that the identified climate variables are important for influencing yellow poplar growth. This information can be used to optimize plantation success in various climatic conditions. Correlation analysis also showed that yellow poplar growth tends to improve with increasing temperature, precipitation, and humidity. This is because the stands are located in regions that correspond to the northern marginal region of the native habitat in North America [15]. Therefore, the maximum values of these variables were considered optimal. 3.5. Effects of Topographic Variables of the Stands on Volume Growth of Yellow Poplar The majority of the stands were established on slopes, classified into three categories: lower slope, mid-slope, and upper slope. Among the 129 plots, 11 were on the lower slope, 43 on the mid-slope, and 75 on the upper slope. Topographic position (TP) on the slope did not show a statistically significant correlation (r = −0.16, p = 0.275) with volume growth (Table 6 and Figure 5). Forests 14 02003 g005 Figure 5. Volume growth of yellow poplar by slope position and wind exposure level. Table 6. Pearson’s correlation coefficients (r) between volume growth of yellow poplar and topographic factors.

Wind exposure (WE), categorized as exposed, neutral (partially exposed), and protected based on facing hills that affected wind flow, showed a moderate negative correlation with volume growth (r = −0.53, p < 0.001, Table 6). Volume growth was significantly lower in exposed wind conditions than in neutral or protected conditions (Figure 5). The effects of WE appeared to be similar to those of altitude, with stands at higher altitudes and those that were more exposed to wind having lower volume growth. Most of the soils in the plots were shallow (55 cm or less), likely due to deforestation before the 1960s and heavy monsoon rains that caused erosion on the steep slopes. However, soil depth did not have a significant correlation with volume growth (r = 0.11, Figure 6). The slope of the stands, soil drainage, and azimuth aspects of the plots did not show any significant correlation with volume growth. Forests 14 02003 g006 Figure 6. Volume growth of yellow poplar by soil depth. The inclination of the stands to the horizon was divided into five classes, but no correlation was observed between slope degree and volume growth (r = −0.06, p = 0.494). Soil drainage, categorized into four classes, showed that drainage was rarely a limiting factor in yellow poplar stands. Additionally, the azimuth directions of the plots did not show any significant difference (p = 0.712) in volume growth. 3.6. Effects of Soil Chemical Properties on Volume Growth of Yellow Poplar The soils in the yellow poplar stands were mostly acidic but generally fertile, with a few exceptions. Six soil chemical properties were investigated: soil pH, organic matter, total nitrogen, exchangeable potassium, exchangeable calcium, and exchangeable magnesium (Table 7). The only soil chemical property that showed a significant correlation with volume growth rankings was phosphorus pentoxide (P2O5) content, which had a moderate negative correlation (r = −0.39, p = 0.013). None of the other variables demonstrated significant correlations with volume growth rankings. Table 7. Spearman rank correlation coefficients (r) between soil chemical properties and volume growth of yellow poplar.

3.7. Integration of the Environmental Variables with Significant Correlation Coefficients Table 8 shows the most important factors influencing optimal growing sites for yellow poplar in Korea. WE, GT, LN, LT, and SH emerged as the most crucial variables. LT and SH played negative roles in the cooler Northeastern and hot and dry Southeastern regions of the Korean Peninsula, which is consistent with the LN effect. Low LT and SH make the region less than ideal for yellow poplar growth. Table 8. Spearman rank correlation coefficients (r) between variables with high correlation coefficients and volume growth of yellow poplar.

The environmental variables that showed the strongest correlations with volume growth (correlation coefficients of 0.3 or higher) were integrated in equal proportions (Table 8). To ensure that the variables were independent, we performed correlation analysis on all possible pairs of variables before combining them. The four variables LN, WE, GT, and SH were mutually independent, meaning that they had no significant linear relationship with each other. This was confirmed by the fact that the correlation coefficients between all two pairs of variables were less than 0.05. However, the other pairs of variables, WE and LT, LT and SH, AT and GT, and GT and LT, were dependent on each other. The integrated mean values were ranked, and their Spearman rank correlation with volume growth rank was calculated (Table 9). The highest correlation coefficients were observed when WE was integrated with GT, and SH, or LN. This suggests that WE is the primary environmental factor controlling the volume growth of yellow poplar in plantations in Korea, with other factors playing a minor role. Table 9. Spearman correlation between different combinations of variables and volume growth of yellow poplar.

In conclusion, we identified WE, P2O5, LN, GT, LT, AT, and SH as the most critical factors governing optimal growing sites for yellow poplar in Korea. Some of these factors have overlapping effects. 4. Discussion Yellow poplar is a versatile tree that can thrive in a variety of climates, from the harsh winters of the north to the nearly frost-free winters of the south [16]. However, despite the milder climate in some northern regions of Korea than in the native northern limit of yellow poplar in the United States, yellow poplar trees in these regions still appear to be damaged by cold snaps. This suggests that cold temperatures and dry winter air may synergistically damage yellow poplar trees in cold northern regions. Previously, the performance and adaptation of yellow poplar in Korea were studied on a much smaller scale, with only six study sites [1]. Air and soil humidity were reported as critical factors for yellow poplar plantations. In this study, we found that regional effects on the growth of yellow poplar are influenced by a variety of climatic variables, with temperature being the most important factor. Yellow poplar grows best when rainfall is well-distributed throughout the growing season [17]. The dry winter and spring in Korea could, therefore, hinder the growth and adaptation of yellow poplar trees in this non-native environment. Previous studies found that global variation in xylem hydraulic conductivity is independently driven by two climate variables: growing season temperature (GT) and growing season precipitation (GP) [18,19]. However, in this study, GP was negatively correlated with volume growth, possibly due to concentrated rainfall during the summer. The only humidity variable significantly correlated with volume growth ranking was SH, which had a negative correlation (r = −0.33, p < 0.05). GH had a weak correlation (r = 0.27, p = 0.065). In ranking correlation, a negative value indicates a positive relationship. This is consistent with findings that higher humidity is beneficial for volume growth during the dry spring season, but can have a negative impact during the wet growing season, possibly by reducing transpiration [20]. Topographical characteristics are also known to play a major role in determining where different tree species are found and how fast they grow [21,22]. However, in this study, only wind exposure showed a significant correlation with yellow poplar volume growth. A study of five mountain regions on three continents found that seedling abundance at alpine tree lines is limited by a combination of factors, with temperature playing a relatively minor role. The strongest negative effects on seedling abundance were found from wind exposure and radiation stress, followed by elevation-related temperature [23]. In our study, none of the other topographical variables, including azimuth aspects, were significantly correlated with volume growth. This is in contrast to previous studies, which have found associations between volume growth and certain aspects of azimuth direction [8,9]. More work is needed to elucidate the azimuthal effects. The positive effects of phosphorus nutrition on tree growth are well-known [24,25]. However, it is not yet clear why yellow poplar volume growth decreases with increased P2O5 levels, given that P2O5 is rarely found in excess in the study area. Soil phosphorus can negatively influence plant growth, as evidenced by a study that found that adding phosphorus to the soil significantly decreased the survival rate of seedlings [26]. We did not measure the phosphorus (P) content in the leaves of the trees. More research is needed to better understand the relationship between P content and volume growth of yellow poplar. Despite these challenges, yellow poplar thrives in more sites than expected. One challenge that may arise in the future is soil depth. Soils in Korean forests are generally shallow, and the majority of yellow poplar trees in the stands are less than 30 years old. As these trees grow larger, they will need deeper soil to support their growth. If the soil is not deep enough, the trees may eventually stop growing or become malformed [27]. Adopting a shorter harvesting rotation could be one way to address the challenge of shallow soil. A recent study employed a Bayesian Generalized Linear Model (BGLM) with climatic variables to predict the habitat distribution of a rare species (Taxus globosa) [28]. In our study, we specifically examined the correlation between yellow poplar growth and abiotic factors. However, we anticipate that in the future, we may employ modeling techniques to comprehensively identify the pivotal factors that influence yellow poplar growth. It will enable us to determine suitable sites for yellow poplar cultivation in Korea. 5. Conclusions Yellow poplar can be successfully cultivated throughout South Korea, but it is important to consider specific environmental factors. For example, yellow poplar should be planted in less wind-exposed sites to avoid damage from cold snaps, especially in northern regions. Conversely, the poor growth observed in the southeast can be attributed to low precipitation and humidity during the spring and growing seasons. South Korea’s concentrated summer precipitation, unlike the consistent year-round precipitation in the eastern part of North America, poses challenges for yellow poplar adaptation in the drier southeast regions. This study identified seven environmental variables with moderate correlations to yellow poplar volume growth. Among these variables, wind exposure (WE), phosphorus pentoxide (P2O5), latitude (LN), growing season temperature (GT), low extreme temperature during January and February (LT), annual temperature (AT), and spring humidity (SH) emerged as the most crucial factors shaping yellow poplar growth, with WE being the most critical. However, the correlation coefficient of the integrated variables remained rather low, possibly due to human interventions, such as fertilization, thinning, or weeding, in artificial stands. These interventions might mask the true effects of environmental variables on tree growth. Nevertheless, the identified variables can serve as valuable tools in guiding the selection of suitable planting zones for yellow poplar. Additionally, understanding the intricate interactions among these environmental factors will enhance our comprehension of tree growth dynamics. By considering the key environmental factors, stakeholders can implement strategic planting practices and management strategies to optimize yellow poplar growth and adaptation in diverse regions of the country.

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