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Article

Screening and a Comprehensive Evaluation of Pinus elliottii with a High Efficiency of Phosphorus Utilization

by
Huan Liu
1,2,†,
Zhengquan He
1,†,
Yuying Yang
2,
Yazhi Zhao
1,2,
Huiling Chen
1,2,
Shuxin Chen
2,
Shaoze Wu
2,
Qifu Luan
2,
Renying Zhuo
2 and
Xiaojiao Han
2,*
1
Key Laboratory of Three Gorges Regional Plant Genetic & Germplasm Enhancement (CTGU), Biotechnology Research Center, China Three Gorges University, Yichang 443002, China
2
State Key Laboratory of Tree Genetics and Breeding, Zhejiang Key Laboratory of Forest Genetics and Breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2025, 16(8), 1291; https://doi.org/10.3390/f16081291
Submission received: 27 June 2025 / Revised: 26 July 2025 / Accepted: 6 August 2025 / Published: 7 August 2025
(This article belongs to the Section Forest Ecophysiology and Biology)
Browse Figures
Versions Notes

Abstract

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities.

1. Introduction

Phosphorus (P) is an essential element in vital substances, such as nucleic acids, proteins, and phospholipids in plants [1]. It plays a significant role in various biological processes, including nucleic acid synthesis, photosynthesis, respiration, cell division and proliferation, biofilm synthesis and stabilization, enzyme activation and inactivation, and signal transduction [2,3]. Plants acquire P from the soil solution as orthophosphate (Pi), such as H 2 P O 4 − [1,4,5], but can only absorb Pi effectively in limited amounts due to its tendency to become immobile and fixed in soils [6]. Plants subjected to Pi-deficiency stress exhibit marked alterations in their morphology, physiology, and biochemistry, impacting plant growth, reproduction, and ultimately yield [7]. Numerous studies have demonstrated that different genotypes within the same species display divergent capacities to activate soil Pi, differing in absorption efficiency and effective P utilization [8,9,10]. Thus, utilizing the genetic resources of P-efficient plants is an effective strategy for addressing the challenge of low Pi soil levels.
In recent years, significant advances have been made in the screening of low Pi-tolerant materials for various crops, including Triticum aestivum L. (wheat) [11], Glycine max (L.) Merr. (soybeans) [12], and Oryza sativa L. (rice) [13]. In wheat, the evaluation of 13 genotypes under two treatments identified 3 genotypes with exceptional phosphorus utilization efficiency (PUE), which was attributed to the expansion of the root system that enhances Pi capture [11]. Similarly, soybean screening—through root phenotyping, Pi content measurement, and cluster analysis—distinguished high- and low-PUE varieties among 90 tested lines [12]. Additionally, Aluwihare et al. [13] classified tolerant and sensitive genotypes based on traits such as PUE in rice. Subsequently, Kumar et al. [14] discovered that the tolerant rice genotype NIL-23 exhibits enhanced root development and an up-regulated expression of the P absorption gene OsPHT1;6 under low Pi conditions, which improves Pi transport efficiency. Furthermore, tomato varieties that are tolerant to low Pi stress enhance leaf and root development, optimize leaf gas exchange, and dynamically regulate PUE [15]. These studies systematically illustrate that low Pi tolerance in crops relies on strategies involving optimizing morphological structures, regulating physiological metabolism, and revealing molecular mechanisms.
Pinus elliottii Engelm var. elliottii (slash pine), an evergreen conifer indigenous to the southeastern United States within the Pinaceae family, has emerged as a crucial silvicultural species in subtropical China since its introduction in the 1930s. This fast-growing pine species demonstrates remarkable ecological adaptability and economic potential, characterized by its exceptional turpentine quality, high timber yield, and significant carbon sequestration capacity. These attributes have driven its rapid proliferation in artificial forest ecosystems across China’s subtropical regions, substantially altering regional forest composition [16]. However, persistent challenges in subtropical plantation management have been exacerbated by chronic P deficiencies resulting from intense lateritic soil weathering processes. Such nutrient limitations have been shown to reduce stand productivity by 30%–45% and significantly compromise resin yield [17]. While current silvicultural research focuses on developing genotypes with high phosphorus use efficiency (PUE), the characteristics and differences in phosphorus utilization among existing slash pine germplasm remain unclear. Thus, the objective of this study was to screen phosphorus-efficient slash pine lines based on plant morphological and physiological traits under low-phosphorus stress, providing a critical basis for screening slash pine family lines with high PUE and offering important references for future research on low-phosphorus tolerance and PUE in slash pine and other forest tree species.

2. Materials and Methods

2.1. Plant Materials and Treatment

Seeds of 13 slash pine family lines were sourced from progenies of superior trees in forest areas of Changle Forest Farm (Xishan Village, Jingshan Town, Yuhang District, Hangzhou, China). Following a five-month greenhouse sowing period (day/night temperature: 20 °C/12 °C; relative humidity: 50%–60%; natural light), seedlings reaching approximately 12 cm in height were used for experimentation. A one-way design was adopted with Pi supply as the sole independent variable: 18 consistent seedlings per family line were randomly assigned to three groups (6 seedlings/group) for distinct Pi treatments—control (normal Pi: 136 mg/kg KH2PO4, NP), low Pi (1.36 mg/kg KH2PO4, LP), and Pi-deficient (0 mg/kg KH2PO4, -P).
All seedlings were cultivated in plastic pots (1 L) containing 1 kg of soil (available Pi: 0.01 mg/kg) supplemented with basic nutrients (mg·kg−1 soil): 506 KNO3, 80 NH4NO3, 241 MgSO4, 36.7 FeNaEDTA, 0.83 KI, 6.2 H3BO3, 16.9 MnSO4·H2O, 8.6 ZnSO4·7H2O, 0.25 NaMoO4·2H2O, 0.025 CuSO4·5H2O, 0.025 CoCl2·6H2O, and 945 Ca(NO3)2·7H2O. Environmental conditions (14 h light/10 h dark cycle, 23–25 °C, 50%–60% humidity) and management (weekly watering) were standardized across treatments, with seedlings randomly positioned in the controlled growth room. After two months of cultivation, determinations included morphological traits (above-ground and below-ground fresh weights) and Pi concentrations in soil, above-ground biomass, and below-ground biomass.

2.2. Measurement of Indicators

2.2.1. Indicators of Plant Morphology

After two months of treatment with varying Pi levels, the height of the slash pine was measured using a ruler. The initial height measured before treatment was subtracted from the final measurement to calculate the plant height gain (PHG). Fresh plants were separated into above-ground and below-ground components at the stem base, weighed, and recorded as above-ground fresh weight (AFW) and below-ground fresh weight (BFW).

2.2.2. Indicators of Root Morphology

After cleaning the roots with distilled water, they were carefully separated to prevent overlap and then placed on specialized root trays. Subsequently, a root scanner (Seiko Epson Corporation, Suwa, Japan) was then employed to scan the plant roots. The scanned root images were analyzed using the WinRHIZO Root Analysis System software (Regent Instruments GGPT-SOP-263, Regent Instruments Inc., Quebec, QC, Canada, V2005) to determine the total root length (TRL), root surface area (RSA), root volume (RV), and the number of root tips (RTs) in the root system.

2.2.3. Determination of Orthophosphate Concentration and Calculation of Phosphorus Utilization Efficiency

The above-ground and below-ground components of slash pine, along with the soil, were dried at 65 °C until they reached a constant weight. Subsequently, the concentrations of available Pi in the above-ground parts (APCs), below-ground parts (BPCs), and soil (SPC) were measured.
Determination of effective Pi concentration in soil [18]: 1.0 g of dried soil was weighed using an electronic balance (BSA223S, Sartorius, Beijing, China) and leached with a solution of ammonium fluoride and hydrochloric acid. This process harnessed the capability of fluoride ions to form complexes with iron and aluminum ions in the acidic solution, facilitating the dissolution of more reactive iron and aluminum salts present in the soil samples. Subsequently, the absorbance of the phosphomolybdenum blue complex was measured at 880 nm using a full-wavelength enzyme marker (Lu-0020-0501, Atila Biosystems, Sunnyvale, CA, USA). The Pi concentration was then calculated based on the standard curve.
Determination method for effective Pi concentration in leaves and roots [19]: About 0.2 g of the dried sample was digested with sulfuric acid and hydrogen peroxide in a digestion instrument (SPH120, Alrva, Jinan, China) to convert organic phosphorus into inorganic phosphate. Under controlled acidity, trivalent antimony ions were added to facilitate the reaction between orthophosphoric acid and ammonium molybdate, resulting in the formation of a ternary heteropoly acid. This compound was then reduced to phosphomolybdenum blue using ascorbic acid. Finally, the absorbance of the phosphomolybdenum blue was measured at 700 nm using a full-wavelength enzyme marker (SpectraMax190, Molecular Devices, San Jose, CA, USA) and the Pi concentration of the sample was calculated using a standard curve. Based on the plant weight and Pi concentration, the bioconcentration factor (BCF), translocation factor (TF), and phosphorus utilization efficiency (PUE) were calculated [15,20].
B i o c o n c e n t r a t i o n   f a c t o r   ( B C F ) = P i   c o n c e n t r a t i o n   i n   t h e   a b o v e - g r o u n d   p a r t s P i   c o n c e n t r a t i o n   i n   s o i l
T r a n s l o c a t i o n   f a c t o r   ( T F ) = A b o v e - g r o u n d   P i   c o n c e n t r a t i o n B e l o w - g r o u n d   P i   c o n c e n t r a t i o n
P h o s p h o r u s   u t i l i z a t i o n   e f f i c i e n c y   ( P U E ) = A b o v e - g r o u n d   b i o m a s s A b o v e - g r o u n d   P i   a c c u m u l a t i o n

2.2.4. Comprehensive Evaluation of Low-Phosphorus Tolerance

The composite assessment value of low-phosphorus tolerance (D), derived using the fuzzy membership function method, was utilized to quantify the P efficiency and stress adaptability of each material under low Pi conditions [21]. The low-phosphorus tolerance factor (LPTF) of a plant is defined as the ratio of a specific indicator measured under low Pi conditions compared to normal Pi treatments. First, calculate the following parameters separately to assess P tolerance: PHG, AFW, BFW, TRL, RSA, RV, RT, APC, BPC, BCF, TF, and PUE. The calculation formula is as follows [12]:
L o w - p h o s p h o r u s   t o l e r a n c e   f a c t o r   ( L P T F ) = M e a s u r e d   v a l u e   o b t a i n e d   d u r i n g   l o w   P i   t r e a t m e n t M e a s u r e d   v a l u e   o b t a i n e d   d u r i n g   n o r m a l   P i   t r e a t m e n t
The value of the affiliation function:
μ ( X i j ) = X i j − X j m i n X j m a x − X j m i n   , i   = 1 , 2 , 3 , … , n ; j   = 1 , 2 , 3 , … , n
where μ(Xij) represents the P efficiency affiliation function of material i for evaluating indicator j, and Xij denotes the measured value of indicator j for material i. Xjmin and Xjmax indicate the minimum and maximum measured values of indicator j, respectively.
Indicator weights:
W j = P j Σ j = 1 n P j   , j   = 1 , 2 , 3 , … , n
where Wj denotes the importance of the jth public factor among all public factors, and Pj represents the contribution of each variety to the jth composite indicator.
Composite assessment value of low-phosphorus tolerance:
D = Σ j = 1 n μ X i j W j ,   i   = 1 , 2 , 3 , … , n ; j   = 1 , 2 , 3 , … , n
where D represents the combined assessed value of P efficiency capacity, derived from the evaluation of each composite index for every slash pine family line under low Pi stress.

2.2.5. Quantitative Real-Time RT-PCR (qRT-PCR) Analysis

Total RNAs were isolated from the roots and leaves of slash pine under normal and low Pi treatments using the RNAprep Pure Plant Plus Kit (DP441, Tiangen, Beijing, China). RNA integrity was assessed by 1.0% (p/v) agarose gel electrophoresis, and concentrations were tested with a NanoDrop2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). PrimeScript™ RT Master Mix (RR036A, TaKaRa, Dalian, China) was used to synthesize first-strand cDNA. For qRT-PCR, cDNA was 10-fold diluted with deionized water. Primers were designed via Primer3plus (https://www.primer3plus.com/ (accessed on 16 July 2025)) based on CDS sequences from Pinus tabuliformis, a close relative of slash pine. All primers and CDS sequences are listed in Supplementary Tables S1 and S2, respectively. Subsequently, qRT-PCR was conducted on a 7300 Real-Time PCR system (Applied Biosystems, Foster, CA, USA). Expression levels were normalized using the endogenous reference gene ubiquitin (UBI) and calculated using the 2−ΔΔCt method [22].

2.3. Statistical Analysis

2.3.1. One-Way ANOVA

All data were analyzed using SPSS software (V27) with one-way ANOVA to test for overall differences among groups, followed by the LSD post hoc test for mean comparisons (p < 0.05). Graphical work was carried out using GraphPad Prism (V9.0).

2.3.2. Principal Component Analysis

The LPTFs for 12 indicators of 13 slash pine family lines were subjected to principal component analysis (PCA) using the SPSS statistical software package (V27), with KMO > 0.6 and p < 0.05, and the cumulative contribution rates of each principal component were obtained. Subsequently, OriginPro (V2024) was used to plot the contribution rates of the comprehensive index for slash pine under varying Pi levels across different treatments [23].
The LPTFs for 12 indicators of 13 slash pine family lines were used to plot principal component dispersion points for the low-P tolerance index via the principal component analysis package (V2017) in OriginPro (V2024) [23].

2.3.3. Correlation Analysis

The D value was calculated using the fuzzy membership function method. The LPTFs for 12 indicators of 13 slash pine family lines were used to plot the correlation heatmap between LPTFs and D via the Correlation Plot package (V2020b) in OriginPro (V2024) [23].

2.3.4. Cluster Analysis

The LPTFs of the indicators screened from principal component analysis and correlation analysis were used to plot a cluster analysis heatmap via the Heat Map with Dendrogram package (V2016) in OriginPro (V2024), using the squared Euclidean distance and the furthest neighbor method [23].

3. Results

3.1. Response of Slash Pine Phenotypic Traits to Varying Levels of Orthophosphate Supply

As illustrated by the height variations among slash pine during the treatment period, significant differences in plant height were noted across different lines subjected to different levels of Pi supply (Figure 1). Among them, there was no significant difference in the growth rates of family lines 11 and 33 across varying Pi concentration treatments. In contrast, the growth rates of family lines 9, 16, and 17 were suppressed when subjected to low Pi levels. Particularly, family lines 9 and 17 exhibited significantly reduced growth under both low and Pi-deficient treatments compared to normal Pi conditions. Conversely, family lines 1, 2, 6, 10, 13, 26, and 27 exhibited significantly higher growth rates under low Pi treatment compared to normal Pi treatment. Additionally, the growth rates of different family lines varied even under the same Pi supply level. When provided with normal Pi levels, family lines 9, 16, and 17 displayed the highest growth rates. In contrast, under low Pi supply, line 2 exhibited the most significant growth rate, while under Pi-deficient conditions, line 27 showed the fastest growth rate. These results reveal the genetic variation among slash pine lines in response to Pi stress, providing a basis for screening low-P-tolerant germplasm resources.
Fresh weight plots (Figure 2) indicated that the biomass of different lines responded variably to Pi supply levels. Lines 1, 2, and 26 exhibited higher above-ground fresh weight accumulation under low Pi supply conditions, with line 26 also demonstrating greater below-ground fresh weight accumulation. In contrast, the above-ground and below-ground biomass of lines 13 and 27 increased as Pi supply levels decreased. Additionally, the above-ground biomass of both lines 9 and 33 increased with rising Pi supply levels, while there was no significant difference between the above-ground and below-ground fresh weights of line 9 under normal and low Pi treatments; however, a more pronounced decrease was noted under Pi-deficiency conditions.
Forests 16 01291 g002
Figure 2. Fresh weight of above-ground and below-ground parts of slash pine under different Pi treatments. (A) Above-ground fresh weight (AFW). (B) Below-ground fresh weight (BFW).
Figure 2. Fresh weight of above-ground and below-ground parts of slash pine under different Pi treatments. (A) Above-ground fresh weight (AFW). (B) Below-ground fresh weight (BFW).
Forests 16 01291 g002

3.2. Root Morphological Indicators of Slash Pine in Response to Varying Levels of Orthophosphate Supply

By analyzing the root morphology of slash pine (Figure 3), we found differences in the response of root morphology to varying levels of Pi supply among the family lines. Family line 27 exhibited an increasing trend in total root length, root surface area, and total number of root tips in response to decreasing Pi supply levels, while the opposite trend was observed in family line 33. Notably, family line 9 demonstrated higher TRL, RSA, RV, and RT under low Pi treatment compared to both normal Pi and Pi-deficient treatments. Furthermore, the root morphology indices of the nine lines under low Pi treatment were favorable, showing no significant decrease compared to those in the normal Pi treatment.
Forests 16 01291 g003
Figure 3. Root conformation of different lines of slash pine under different Pi treatments. (A) Total root length (TRL). (B) Root surface area (RSA). (C) Root volume (RV). (D) Root tip number (RT).
Figure 3. Root conformation of different lines of slash pine under different Pi treatments. (A) Total root length (TRL). (B) Root surface area (RSA). (C) Root volume (RV). (D) Root tip number (RT).
Forests 16 01291 g003

3.3. Slash Pine Phosphorus Utilization Efficiency

Effective Pi concentrations in the soil exhibited a generally consistent trend with the externally applied concentrations (Figure 4A). Simultaneously, the Pi concentrations in both the above-ground biomass and the root system demonstrated a similar trend (Figure 4B,C). The morphological responses of different lines within the pine family to varying Pi supply levels, as well as the relationships between various morphological indicators and the differences in biomass and Pi uptake among these lines at different Pi levels, are illustrated in the results above. Based on these findings, the BCF (Figure 5A), TF (Figure 5B), and PUE (Figure 5C) were calculated for 13 lines of slash pine. Significant differences were observed in the BCF, TF, and PUE among the various lines.
Specifically, line 2 exhibited a significantly higher Pi absorption capacity under low Pi and Pi-deficient conditions compared to normal P conditions; however, its TF and PUE showed minimal variation (Figure 5). Line 9 demonstrated the highest Pi absorption capacity under low Pi and Pi-deficient conditions, but its translocation capacity under low Pi conditions was significantly lower than that observed under normal Pi and Pi-deficient conditions. Line 11 exhibited relatively strong Pi absorption and translocation capacity under low Pi conditions, although its PUE was low. The BCF and TF of line 17 increased as Pi application levels decreased, and its PUE under low Pi conditions was significantly higher than under normal Pi and low Pi conditions. The BCF of line 26 also increased as Pi application levels decreased, but the trend in PUE was the opposite. The BCF of line 27 under low Pi conditions was significantly higher than under normal Pi and Pi-deficient conditions; however, the TF and PUE showed no significant differences under normal Pi conditions. This indicates that individual data points alone cannot fully reflect the Pi absorption and utilization capacity of slash pine. Therefore, a comprehensive analysis of multiple indicators is necessary.
Forests 16 01291 g004
Figure 4. Pi concentration of slash pine. (A) Soil Pi concentration (SPC). (B) Above-ground Pi concentration (APC). (C) Below-ground Pi concentration (BPC).
Figure 4. Pi concentration of slash pine. (A) Soil Pi concentration (SPC). (B) Above-ground Pi concentration (APC). (C) Below-ground Pi concentration (BPC).
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Forests 16 01291 g005
Figure 5. Bioconcentration factor, translocation factor, and phosphorus utilization efficiency of slash pine. (A) Bioconcentration factor (BCF). (B) Translocation factor (TF). (C) Phosphorus utilization efficiency (PUE). The dashed line in (B) is used to distinguish whether the TF exceeds the threshold of 1.
Figure 5. Bioconcentration factor, translocation factor, and phosphorus utilization efficiency of slash pine. (A) Bioconcentration factor (BCF). (B) Translocation factor (TF). (C) Phosphorus utilization efficiency (PUE). The dashed line in (B) is used to distinguish whether the TF exceeds the threshold of 1.
Forests 16 01291 g005

3.4. Principal Component Analysis of Low-Phosphorus Tolerance Factors for Various Indicators of Slash Pine Family Lines

Principal component analysis of the LPTFs for 12 indicators across 13 slash pine family lines (Figure 6A) revealed that the first principal component accounted for 42.7% of the variance. The absolute values of the eigenvectors for root system phenotypes were greater than those for other traits, suggesting that the first principal component encompassed RSA, TRL, and BFW, which can be collectively summarized as root development factors (Figure 6B). The second principal component contributed 28.0% (Figure 6A), primarily encompassing PUE, APC, and BCF, which can be summarized as P utilization factors (Figure 6B). The cumulative contribution rate of these two principal components reached 70.7%, suggesting that these combined indicators are significantly associated with the low-P tolerance of slash pine.

3.5. Comprehensive Evaluation of Low-Phosphorus Tolerance in Slash Pine Family Lines

The correlation analysis between the low-phosphorus tolerance factors of 12 indicators measured in slash pine and the composite assessment value of low-P tolerance revealed a significant relationship. Specifically, the low-phosphorus tolerance factors for PHG, AFW, BFW, TFW, RSA, RV, RT, APC, BPC, BCF, BF, and PUE were all significantly correlated with the D value (p < 0.05, Figure 7). The correlation coefficients for AFW (0.69), BFW (0.52), TRL (0.56), RSA (0.63), RV (0.67), APC (0.78), BPC (0.52), BCF (0.77), and PUE (−0.76) were notably high, which aligns with the findings from the principal component analysis.
Based on a comprehensive evaluation of PUE and other relevant metrics for various slash pine family lines, the nine low-phosphorus tolerance factors were clustered using the squared Euclidean distance and the furthest neighbor method. The clustering results (Figure 8) indicate that the 13 slash pine family lines can be categorized into three groups: the first group comprises high-P-efficient lines, the second group includes medium-P-efficient lines, and the third group consists of low-P-efficient lines. Under low Pi treatment, the P-high-efficient pine lines were 2, 26, and 27, while the P-low-efficient pine lines were 1, 6, 9, and 13. Under Pi-deficient treatment, the P-high-efficient pine lines were 13 and 27, while the P-low-efficient lines were 1, 6, 9, 11, 16, and 33. Ultimately, a consistent low-P-resistant family line, 27, and three low-P-sensitive family lines, 1, 6, and 9, were identified based on the analysis of low-P tolerance under both low Pi and Pi-deficiency treatments.

3.6. Expression of Phosphorus Utilization-Related Genes

Based on the above analysis, soil Pi content differences, and practical environmental considerations, we measured the relative expression levels of 12 phosphorus starvation-responsive candidate genes in slash pine lines 9 and 27 under normal Pi and low Pi treatments (Figure 9). The results reveal significant tissue specificity and Pi-dependent expression patterns across the 12 genes. Under low Pi treatments, the root tissues of both lines showed a significantly induced expression of Pi balance signaling genes (SPX1, SPX3, SPX4), as well as genes governing Pi uptake (PHT1;1), Pi remobilization (PAP23), and membrane phospholipid remodeling (SQD1, SQD2, NPC4). In leaf tissues, low Pi treatments significantly up-regulated the expression of Pi balance signaling genes (SPX1, SPX3, SPX4), Pi remobilization (PAP23), and membrane phospholipid remodeling (SQD1). Notably, relative to line 9, line 27 exhibited an approximately twofold higher leaf PHT1;1 expression and threefold higher SQD1/2 expression. Even though low Pi treatments down-regulated leaf SQD2 and NPC4 expression in line 27, their levels remained significantly higher than in line 9. These differential gene expression patterns collectively underpin the adaptive regulation of line 27 to low Pi stress.

4. Discussion

Phosphorus is one of the most essential and limiting elements for tree growth and nutrient cycling [24]. Due to deforestation, highly differentiated soils, and heavy rainfall, the subtropical region of China has experienced a significant loss of phosphorus [25], resulting in serious ecological issues in plantation forests [26,27]. Additionally, the excessive application of phosphorus fertilizers can lead to a range of problems, including soil structure degradation, decreased fertility, and environmental pollution [28,29]. Consequently, improving the utilization efficiency of P in plants has garnered increasing attention [30,31]. Therefore, it is crucial to investigate the mechanisms that enable adaptation to low Pi stress through specific tolerance tests and to identify P-efficient lines of slash pine that thrive under low Pi conditions.

4.1. Differences in Slash Pine-Related Traits Under Low Orthophosphate Stress

Plants possess the ability to actively regulate their physiological processes in response to abiotic stresses, a characteristic supported by numerous studies [32]. In this research, three treatments were applied to slash pine: normal Pi supply, low Pi, and Pi deficiency. The results indicate that these different treatments significantly influenced the morphological indices and traits associated with PUE in slash pine, revealing genotype-specific differences in adaptability among lines. As the primary organ responsible for sensing Pi deficiency, the root system increases its contact area with the soil through morphological adjustments, including increased biomass, elongated root length, and proliferated root tips. These adaptations enhance Pi absorption efficiency [33,34]. In this study, low Pi treatment significantly promoted root growth in pine lines 2, 26, and 27, while Pi-deficiency treatment only stimulated root growth in line 27. This indicates that P-efficient lines demonstrate greater root adaptability under stress. Physiological parameter analysis revealed that the bioconcentration factor of plants in the stressed group was significantly higher than that of the control group, confirming that Pi-deficient environments enhance plants’ ability to accumulate Pi from the soil. Most translocation factors were less than one, indicating that Pi primarily accumulates in the roots as a response to stress. As reported in previous studies [35,36], most slash pine lines exhibited reduced above-ground biomass and plant height under low Pi or Pi-deficient conditions, except for family line 27, which displayed a unique phenotype characterized by significantly increased above-ground biomass. This supports the hypothesis that P-efficient lines possess advantages in root exploration under stress conditions [37,38]. Despite lower tissue Pi concentrations during stress, PUE was elevated, reflecting optimized internal reallocation—an adaptive strategy conserved for coping with Pi limitation.

4.2. Screening for Indicators of Low-Phosphorus Tolerance in Slash Pines

The response of plants to low Pi stress is an extremely complex physiological and ecological process. Low-P tolerance is a quantitative trait controlled by multiple genes and influenced by various factors, making it challenging to use a single trait as a standard for evaluating plant tolerance to low P [21]. To comprehensively assess a crop’s low-P tolerance, the academic community employs a multi-indicator assessment system, where the selection of scientific indicators is crucial. Aluwihare et al. [13] evaluated P efficiency in rice by analyzing plant height, above-ground dry weight, Pi concentration, and P utilization efficiency, systematically examining physiological responses to varying Pi levels with a focus on material accumulation and storage. In a subsequent study, Lu et al. [21] utilized factor analysis to identify stem thickness, total root volume, above-ground dry weight, root projected area, and leaf Pi concentration as core indicators for assessing low-P tolerance in Gleditsia sinensis Lam.; this approach revealed inter-indicator relationships through dimensionality reduction. Similarly, Tantriani et al. [39] screened low-P-tolerant and sensitive soybean varieties based on shoot dry matter, P utilization efficiency, root morphological traits (e.g., root dry weight and root length), and allele sharing distance metrics. In this study, we evaluated the low-P tolerance of slash pine seedlings by transforming twelve indices related to biomass, root morphology, and P utilization efficiency into two principal components, which accounted for a cumulative contribution of 70.7%. We then employed the fuzzy comprehensive evaluation to calculate the composite assessment value of low-P tolerance, integrating index weights and membership degrees. This value comprehensively considered the weights and membership levels of each indicator, thereby more accurately reflecting the tolerance of slash pine under low Pi stress. Based on this analysis, we analyzed the correlation between the twelve P efficiency indicators and the composite assessment value of low-P tolerance. As a result, nine indicators—above-ground fresh weight, below-ground fresh weight, total root length, root surface area, root volume, above-ground Pi concentration, below-ground Pi concentration, bioconcentration factor, and P utilization efficiency—were precisely identified as the core indicator system for evaluating the low-P tolerance of slash pine. This provides a scientific and reliable theoretical foundation, as well as technical support, for the breeding and cultivation management of low-P-tolerant slash pine family lines.

4.3. Comprehensive Evaluation of Phosphorus Efficiency in Slash Pine

In studies focused on screening P-efficient germplasm resources in plants, the comprehensive P efficiency value has been widely recognized as a key indicator across various research systems. For instance, Wang et al. [12] and Lu et al. [21] employed precise calculations of this value to identify P-tolerant soybean varieties and P-efficient Gleditsia sinensis Lam., respectively. This study adopted a similar approach, utilizing the P efficiency composite value for cluster analysis to successfully identify the slash pine high-P-efficient line 27, along with three low-P-efficient lines: 1, 6, and 9. When plants experience Pi-deficiency stress, they initiate a series of physiological responses [40,41], including the redistribution of P in stems and roots, the regulation of P transport proteins, the remodeling of root morphology (e.g., the development of lateral roots and root hairs), and the reorganization of carbon metabolism [42,43,44]. Among these responses, P hunger signal transduction can stimulate an increase in root surface area to enhance Pi uptake capacity [45]. However, some studies have indicated that P-efficient wheat varieties primarily rely on greater biomass accumulation to maintain yield under Pi-deficient soil conditions, rather than through root elongation and development [46]. Notably, the family line 27 exhibits a distinct response under low Pi or Pi-deficient conditions: its growth height, fresh weight, and P utilization efficiency were significantly superior to those of the control group, and the above-ground biomass in the low Pi treatment group exceeded that of the normal Pi-supply group. This phenotype challenges the traditional understanding that Pi deficiency inhibits above-ground growth, suggesting that this family may possess a unique metabolic regulatory network capable of precisely coordinating root and above-ground development processes, thereby alleviating the constraints of Pi deficiency on biomass accumulation.

4.4. Potential Mechanisms for the Adaptation of Slash Pine Family Line 27 to Low-Phosphorus Environments

Phosphate levels influence plant Pi uptake, transport, remobilization, and signaling processes by regulating differential gene expression [47]. In this study, both slash pine family lines 9 and 27 activated low-P signaling through a high expression of root SPX1, SPX3, and SPX4 genes. This aligns with the findings of Wang et al. [48], who demonstrated SPX family regulation of phosphate homeostasis in alfalfa. Meanwhile, enhanced root Pi uptake via a high expression of PHT1;1 supports Yang et al.’s [2] conclusion that PHT1 functions as a key Pi transporter. The down-regulation of PHO1 and up-regulation of PHO2 lead to the preferential retention of Pi in roots, aligning with the Pi allocation mechanism described in Arabidopsis [43]. The significant up-regulation of PAP23 further corroborates Guo et al.’s (2025) [49] finding that the PAP family promotes Pi remobilization.
However, the leaves of line 27 exhibit specific regulation under low-phosphate conditions: a high expression of PHT1;1—approximately twice that of family line 9—enhances above-ground Pi uptake, consistent with the characteristics of P-efficient genotypes observed in soybean [50]. The synergistic effect of high expressions of SQD1 and SQD2 (about three times that of family line 9) combined with the down-regulation of NPC4 aligns with the membrane lipid remodeling mechanism that reduces Pi consumption, as proposed by Yang et al. [51]. This regulatory pattern optimizes the remobilization of stored Pi, reduces membrane damage, and prioritizes allocation to critical metabolic processes. Through these integrated mechanisms that improve leaf PUE, line 27 achieves a robust adaptation to low Pi environments.

5. Conclusions

In this study, key indicators for screening high-P-efficient slash pine lines include above-ground fresh weight, below-ground fresh weight, total root length, root surface area, root volume, as well as above-ground Pi concentration, below-ground Pi concentration, bioconcentration factor, and P utilization efficiency. Through principal component analysis, fuzzy membership function methods, and cluster analysis, 13 slash pine family lines were classified into high-P-efficient, medium-P-efficient, and low-P-efficient groups. Family line 27 was categorized in the high-P-efficient group, while lines 1, 6, and 9 were placed in the low-P-efficient group. Both lines 9 and 27 exhibit strong root responsiveness to low Pi environments. However, line 27 uniquely adapts by enhancing leaf Pi allocation and remobilization to maintain above-ground Pi levels, significantly promoting stem growth and leaf biomass accumulation—traits that are absent in line 9, which demonstrates the opposite trend. This exceptional performance positions line 27 as an ideal candidate for breeding low-P-tolerant pine varieties. Additionally, line 27 exhibited a stronger regulatory capacity with higher expression of phosphorus starvation response genes. Subsequent long-term field trials can be conducted in representative low Pi soils to evaluate the growth performance, P utilization efficiency, and ecological adaptability of line 27 under natural conditions, thereby supporting large-scale forestry applications, reducing dependence on phosphorus fertilizers, lowering costs, and mitigating the risk of eutrophication.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16081291/s1, Table S1: Primer sequences for qRT-PCR; Table S2: Referenced transcriptome sequences of Pinus tabuliformis for qRT-PCR.

Author Contributions

Methodology, H.L., Z.H., Q.L. and R.Z.; Data curation, H.L., Y.Y., Y.Z., H.C., S.C. and S.W.; Writing—original draft, H.L.; Writing—review & editing, X.H. All authors have read and agreed to the published version of the manuscript.

Funding

Supported by the major projects in scientific innovation, Ministry of Science and Technology, the People’s Republic of China (2023ZD0405805) to X.H.

Data Availability Statement

The original contributions presented in this study are included in the article and Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Height differences in slash pine under various Pi treatments. (A) Growth of 13 slash pine lines after two months of treatment with three different Pi concentrations. Scale bars: 2.0 cm. (B) The difference between post-treatment and pre-treatment above-ground heights of slash pine under different Pi concentrations is defined as plant height gain (PHG). Annotation: Thirteen family lines were arranged in ascending order. The data presented in the figure represents the mean ± standard deviation. Different capital letters indicate significant differences based on the LSD test (p < 0.05) among normal orthophosphate (NP), low orthophosphate (LP), and orthophosphate-deficiency (-P) supply levels for the same family. Additionally, different lowercase letters signify significant differences based on the LSD test (p < 0.05) among different family lines at the same Pi supply level. The same annotation also applies to Figure 2, Figure 3, Figure 4 and Figure 5.
Figure 1. Height differences in slash pine under various Pi treatments. (A) Growth of 13 slash pine lines after two months of treatment with three different Pi concentrations. Scale bars: 2.0 cm. (B) The difference between post-treatment and pre-treatment above-ground heights of slash pine under different Pi concentrations is defined as plant height gain (PHG). Annotation: Thirteen family lines were arranged in ascending order. The data presented in the figure represents the mean ± standard deviation. Different capital letters indicate significant differences based on the LSD test (p < 0.05) among normal orthophosphate (NP), low orthophosphate (LP), and orthophosphate-deficiency (-P) supply levels for the same family. Additionally, different lowercase letters signify significant differences based on the LSD test (p < 0.05) among different family lines at the same Pi supply level. The same annotation also applies to Figure 2, Figure 3, Figure 4 and Figure 5.
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Figure 6. Principal component analysis of different indicators at the different Pi supply level of slash pine family lines. (A) Principal component dispersion points for the low-P tolerance index. (B) The coefficients and contribution rates of the comprehensive index for slash pine under varying Pi levels across different treatments. Red indicates a positive correlation, blue indicates a negative correlation, and the depth of color corresponds to the absolute value of the correlation coefficient. Plant height gain (PHG), above-ground fresh weight (AFW), below-ground fresh weight (BFW), total root length (TRL), root surface area (RSA), root volume (RV), root tip number (RT), above-ground orthophosphate concentration (APC), below-ground orthophosphate concentration (BPC), bioconcentration factor (BCF), translocation factor (TF), and phosphorus utilization efficiency (PUE).
Figure 6. Principal component analysis of different indicators at the different Pi supply level of slash pine family lines. (A) Principal component dispersion points for the low-P tolerance index. (B) The coefficients and contribution rates of the comprehensive index for slash pine under varying Pi levels across different treatments. Red indicates a positive correlation, blue indicates a negative correlation, and the depth of color corresponds to the absolute value of the correlation coefficient. Plant height gain (PHG), above-ground fresh weight (AFW), below-ground fresh weight (BFW), total root length (TRL), root surface area (RSA), root volume (RV), root tip number (RT), above-ground orthophosphate concentration (APC), below-ground orthophosphate concentration (BPC), bioconcentration factor (BCF), translocation factor (TF), and phosphorus utilization efficiency (PUE).
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Figure 7. Correlation analysis between low-phosphorus tolerance factor and composite assessment value of low-phosphorus tolerance (D) of slash pine traits. Plant height gain (PHG), above-ground fresh weight (AFW), below-ground fresh weight (BFW), total root length (TRL), root surface area (RSA), root volume (RV), root tip number (RT), above-ground orthophosphate concentration (APC), below-ground orthophosphate concentration (BPC), bioconcentration factor (BCF), translocation factor (TF), and phosphorus utilization efficiency (PUE). Red indicates a positive correlation, blue indicates a negative correlation, and the depth of color corresponds to the absolute value of the correlation coefficient. * indicates that the correlation between the indicators reached a significant level (p < 0.05), and ** indicates that the correlation between the indicators reached a highly significant level (p < 0.01).
Figure 7. Correlation analysis between low-phosphorus tolerance factor and composite assessment value of low-phosphorus tolerance (D) of slash pine traits. Plant height gain (PHG), above-ground fresh weight (AFW), below-ground fresh weight (BFW), total root length (TRL), root surface area (RSA), root volume (RV), root tip number (RT), above-ground orthophosphate concentration (APC), below-ground orthophosphate concentration (BPC), bioconcentration factor (BCF), translocation factor (TF), and phosphorus utilization efficiency (PUE). Red indicates a positive correlation, blue indicates a negative correlation, and the depth of color corresponds to the absolute value of the correlation coefficient. * indicates that the correlation between the indicators reached a significant level (p < 0.05), and ** indicates that the correlation between the indicators reached a highly significant level (p < 0.01).
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Figure 8. Results of cluster analysis of low-P tolerance of slash pine family lines under different Pi treatments. (A) Cluster analysis of various slash pine family lines subjected to low Pi treatment. (B) Pi-deficiency treatment. I. High-phosphorus-efficient lines; II. Medium-phosphorus-efficient lines; III. Low-phosphorus-efficient lines. Red indicates a positive correlation, blue indicates a negative correlation, and the depth of color corresponds to the absolute value of the correlation coefficient. Above-ground fresh weight (AFW), below-ground fresh weight (BFW), total root length (TRL), root surface area (RSA), root volume (RV), above-ground orthophosphate concentration (APC), below-ground orthophosphate concentration (BPC), bioconcentration factor (BCF), and phosphorus utilization efficiency (PUE).
Figure 8. Results of cluster analysis of low-P tolerance of slash pine family lines under different Pi treatments. (A) Cluster analysis of various slash pine family lines subjected to low Pi treatment. (B) Pi-deficiency treatment. I. High-phosphorus-efficient lines; II. Medium-phosphorus-efficient lines; III. Low-phosphorus-efficient lines. Red indicates a positive correlation, blue indicates a negative correlation, and the depth of color corresponds to the absolute value of the correlation coefficient. Above-ground fresh weight (AFW), below-ground fresh weight (BFW), total root length (TRL), root surface area (RSA), root volume (RV), above-ground orthophosphate concentration (APC), below-ground orthophosphate concentration (BPC), bioconcentration factor (BCF), and phosphorus utilization efficiency (PUE).
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Figure 9. Relative expression levels of phosphorus starvation response genes in slash pine family lines 9 and 27 under normal phosphorus and low-phosphorus treatments. Different uppercase letters indicate significant differences in the roots or leaves of the same family under normal phosphate (NP) and low phosphate (LP) supply levels (based on LSD test, p < 0.05). In addition, different lowercase letters indicate significant differences between the roots or leaves of family lines 9 and 27 under the same phosphorus supply level (based on LSD test, p < 0.05). The dashed line in the figure serves to distinguish between root and leaf.
Figure 9. Relative expression levels of phosphorus starvation response genes in slash pine family lines 9 and 27 under normal phosphorus and low-phosphorus treatments. Different uppercase letters indicate significant differences in the roots or leaves of the same family under normal phosphate (NP) and low phosphate (LP) supply levels (based on LSD test, p < 0.05). In addition, different lowercase letters indicate significant differences between the roots or leaves of family lines 9 and 27 under the same phosphorus supply level (based on LSD test, p < 0.05). The dashed line in the figure serves to distinguish between root and leaf.
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Liu, H.; He, Z.; Yang, Y.; Zhao, Y.; Chen, H.; Chen, S.; Wu, S.; Luan, Q.; Zhuo, R.; Han, X. Screening and a Comprehensive Evaluation of Pinus elliottii with a High Efficiency of Phosphorus Utilization. Forests 2025, 16, 1291. https://doi.org/10.3390/f16081291

AMA Style

Liu H, He Z, Yang Y, Zhao Y, Chen H, Chen S, Wu S, Luan Q, Zhuo R, Han X. Screening and a Comprehensive Evaluation of Pinus elliottii with a High Efficiency of Phosphorus Utilization. Forests. 2025; 16(8):1291. https://doi.org/10.3390/f16081291

Chicago/Turabian Style

Liu, Huan, Zhengquan He, Yuying Yang, Yazhi Zhao, Huiling Chen, Shuxin Chen, Shaoze Wu, Qifu Luan, Renying Zhuo, and Xiaojiao Han. 2025. "Screening and a Comprehensive Evaluation of Pinus elliottii with a High Efficiency of Phosphorus Utilization" Forests 16, no. 8: 1291. https://doi.org/10.3390/f16081291

APA Style

Liu, H., He, Z., Yang, Y., Zhao, Y., Chen, H., Chen, S., Wu, S., Luan, Q., Zhuo, R., & Han, X. (2025). Screening and a Comprehensive Evaluation of Pinus elliottii with a High Efficiency of Phosphorus Utilization. Forests, 16(8), 1291. https://doi.org/10.3390/f16081291

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