Next Article in Journal
How Did the Comprehensive Commercial Logging Ban Policy Affect the Life Satisfaction of Residents in National Forest Areas? A Case Study in Northeast China and Inner Mongolia
Next Article in Special Issue
Effects of Forest Gaps on Forest Floor Microbial Community Composition in Pinus tabulaeformis Forests in a Rocky Mountain Area, Beijing, China
Previous Article in Journal
Full-Length Transcriptome Sequencing and Identification of Hsf Genes in Cunninghamia lanceolata (Lamb.) Hook
Previous Article in Special Issue
Divergent Effects of Fertilizer Regimes on Taxonomic and Functional Compositions of Rhizosphere Bacteria and Fungi in Phoebe bournei Young Plantations Are Associated with Root Exudates
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 4
10.3390/f14040685
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Microbial Communities on Elevational Gradient Adaptation Strategies of Pinus yunnanensis Franch. and Pinus densata Mast. in a Mixed Zone

by
Dejin Mu
1,2,
Junrong Tang
1,2,
Nianhui Cai
1,2,
Shi Chen
1,2,
Yingnian He
1,2,
Zijun Deng
2,
Yi Yang
2,
Dan Yang
2,
Yulan Xu
1,2 and
Lin Chen
1,2,*
1
Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China
2
College of Forestry, Southwest Forestry University, Kunming 650224, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(4), 685; https://doi.org/10.3390/f14040685
Submission received: 20 February 2023 / Revised: 21 March 2023 / Accepted: 25 March 2023 / Published: 27 March 2023
(This article belongs to the Special Issue Microbial Community Composition and Function in Forest Soil)
Browse Figures
Versions Notes

Abstract

:
Pinus densata Mast. is considered a homoploid hybrid species that originated from the putative parent species Pinus tabuliformis Carr. and Pinus yunnanensis Franch., but the mechanism of the adaptive differentiation of P. densata and its parents in native habitats has not been reported. Therefore, the overlapping distribution areas between P. densata and P. yunnanensis in the heart of the Hengduan Mountains were chosen. The adaptive differentiation mechanism of the homoploid hybrids and their parents with respect to the elevational gradient was studied based on the morphological features and the different strategies of recruiting endophytic microbial communities from the rhizosphere soil. The results showed that (1) the height and diameter at breast height were the greatest at 2600 m and 2900 m, and from 2700 m to 2900 m, three-needle pines (P. yunnanensis-like type) transitioned into two-needle pines (P. densata-like type). (2) The recruitment of rhizosphere microbial communities was driven by the C, N, P and pH values which showed significant elevation features. (3) There was a significant difference in the recruitment strategies of endophytes between the P. yunnanensis-like type and P. densata-like type. Pinus densata mainly reduced the recruitment of Mucoromycota (fungi) and increased the recruitment of Proteobacteria (bacteria), which may be related to environmental adaptability, quorum sensing and the metabolism of auxiliary factors and vitamins at high elevations. (4) The root endophytic microbiome was enriched in the rare groups from the rhizosphere soil microbial pool. The results of this study provide new insights and new ideas for environmental adaptability and differentiation in homoploid hybrid speciation.

1. Introduction

Mountain ecosystems are excellent systems to study microbial communities because many climate and soil variables change within a relatively small distance [1]. Elevational gradients usually alter soil properties, including pH, organic matter content, water content, total nitrogen, phosphorus, potassium, available nitrogen, phosphorus and potassium, which differentiate niches [2,3]. Soil microbial elevational diversity patterns have been extensively studied in mountain ecosystems [4]. Soil pH is considered to have the strongest direct impact on the diversity and community structure of soil microorganisms [5,6,7,8]. On a relatively large geographical scale, soil characteristics, soil quality, season, climate, elevation and other factors change the soil microbial pool, thus affecting the recruitment of root endophytes [9,10]. However, the elevational gradients of mountain ecosystems, rapidly alter both biotic and abiotic environments, are an ideal system in which to examine how plant hosts recruit microbial symbionts with changes in environmental conditions.
The Qinghai–Tibet Plateau (QTP) sensu lato (sl) comprises the plateau, the Himalayas and the Hengduan Mountains, is known as the ‘Roof of the World’, has the richest species diversity in the Northern Hemisphere and exhibits a high level of endemism [11]. Conifers, which constitute more than 39% of the world’s forests, are prominent components of the QTP sl [12]. It remains unknown why the mountainous areas at middle latitudes contain the most coniferous species [13]. The QTP sl is an important site for species diversity and speciation of conifers. Alpine Pinus densata (Pinaceae) is one of the prominent species on the QTP sl, with a high drought stress tolerance and adaptation to high elevation habitats [11]. Pinus densata Mast. is considered a type species of homoploid hybrid speciation (HHS) that originated from the putative parent species Pinus tabuliformis Carr. and Pinus yunnanensis Franch. [14]. Evidence from molecular genetics, for example, chloroplast DNA polymorphism [15], allozyme [16,17,18], simultaneous DNA probes of the 18S rRNA gene and the 5S rRNA gene [19], the paternally inherited chloroplast rbcL gene and maternally inherited mitochondrial nad1 fragments [20,21], demonstrates that P. densata is a natural homoploid hybrid and that ecological selection may have played a role in its speciation. Approximately 55 million years ago, the Tibetan Plateau supposedly rose as the Indian continent smashed into Eurasia [22], and P. densata were separated from their parents. As the QTP sl rose, the hybrid was mainly distributed at high elevations on the Tibetan Plateau, mainly in the Himalayas, and extended to the Hengduan Mountains. Its two parents are currently mainly distributed in low-elevation Yunnan Province and northern China, with distinct niches [11]. At present, P. densata and its parents P. tabuliformis and P. yunnanensis are unlikely to coexist in a small geographical area, and in the overlapping areas it is difficult to find even P. densata and one of its parents [23]. There may be a small number of coexisting communities of P. densata and P. yunnanensis in the heart of the Hengduan Mountains, Yunnan Province [23]. A report showed weak genetic barriers among P. densata and its parental species, as revealed by crossing experiments [24]. Therefore, the differences between the microbial, chemical and morphological characteristics of P. densata and one of its parents in a mountainous system will be helpful in revealing the niche differentiation of different species of pines from the perspective of microenvironment adaptability. Additionally, the identification and isolation of beneficial microorganisms will be beneficial to enhance the drought and cold resistance of forest seedlings.
The microbial communities of plants that promote growth [25], nutrient uptake [26], stress tolerance [27] and resistance to pathogens [28] play many ecological roles. However, only a few studies have focused on plant microbial community assembly along elevational gradients. For example, in Baima Snow Mountain (28°35′–27°24′ N, 98°55′–99°24′ E, Yunnan Province, China), the heterogeneity of the richness and community composition of ectomycorrhizal fungi was stronger among different elevational zones than across different seasons [29]. Additionally, Zi et al., (2020) reported that multiple soil factors, especially the content of soil ammonium nitrogen (NH4+-N), were the key factors that shaped the bacterial community of ancient wild tea plant roots along elevational gradients [30]. A Pinus densiflora forest study showed that elevation negatively affected the species richness of root-inhabiting fungi but did not influence that of soil-inhabiting fungi [31]. However, the recruitment strategy of the symbiotic microbial communities and the functions of the microorganisms in niche differentiation associated with elevational gradients on the scale of a mountain have not been reported.
Here, we can infer that P. densata and P. yunnanensis can coexist in relatively narrow ranges and that the pines in this area exhibit the composite features of P. densata and P. yunnanensis. This mixed zone of P. densata and P. yunnanensis provides a good sample plot for the study of niche differentiation of P. densata and P. yunnanensis in microhabitats. Therefore, we analyzed the rhizosphere and endophytic microbiomes of P. densata and P. yunnanensis using next-generation sequencing and measured the morphological features of pines. We hypothesized that (H1) pine trees recruit specific microorganisms from rhizosphere soil as endophytes. Endophytes are related to immunity and metabolism, and rhizosphere microbes are related to the interaction between microbes and the host. (H2) P. densata has both parental characteristics and developed typical characteristics of high elevation adaptation. P. densata grows well at high elevations, and its needles show the characteristics of water holding and drought resistance to adapt to high elevations. P. densata roots recruit a series of drought-resistant, enhanced metabolism and disease-resistant microorganisms to enhance their adaptability to high elevations.

2. Materials and Methods

2.1. Site Description and Sample Processing

The samples were collected in Xianggelila City, Yunnan Province, 4 August 2021. August is the most prosperous season for plant growth in Yunnan Province. The sampling sites were located in the heart of the Hengduan Mountains, Xianggelila City, Yunnan Province, China (27°45′44.42″ N, 100°5′38.91″ E). In this study, sampling sites were chosen every 200 m over an elevation gradient, for a total of six representative elevation gradients: 2300 m, 2500 m, 2700 m, 2900 m, 3100 m and 3300 m. According to the flora of China, P. yunnanensis and P. densata have overlapping areas at 2600 m elevation [32]; therefore, 2600 m was also selected, and a total of seven representative elevations were studied (Supplementary Table S1). Within each elevational band, four plots (20 × 20 m) were randomly selected, separated by at least 100 m from each other (Supplementary Figure S1, Supplementary Table S1). Major plant types were investigated in each plot, and the coverage of major plant components was estimated (Supplementary Table S2). The diameter at breast height (DBH) and tree height of 12 pine trees with DBH greater than 5 cm were randomly selected and recorded in each plot (trees with DBH ≤ 5 cm were considered saplings, Supplementary Figure S2). The DBHs were divided into different diameter classes, such as the 6 cm class (5.0 cm ≤ DBH ≤ 6.9 cm) and the 8 cm class (7.0 cm ≤ DBH ≤ 8.9 cm).
According to the difficulty of sampling and the planting time, two pines (DBH 6–10 cm) growing for 10–15 years in each plot were randomly selected, and the morphological features of the needles, chemical properties and microbial communities of the rhizosphere soil and roots were then collected. The mature needles of 8 pine trees at each elevation were collected and transported to the laboratory. Fifty clusters of each pine were randomly selected, and the numbers of two- and three-needle clusters, needle length, needle diameter and needle cluster diameter were measured.
For collection of the rhizosphere soil, after removal of the weeds and topsoil in the sampling spots with spades, the rhizosphere soil samples were taken from the root-rich areas in the 10–30 cm depth range from two opposite directions of the pine trees. After removal of loose soil from root surfaces and gravel from root crevices, the soil from root surfaces was gently collected using a 2 mL frozen tube (Supplementary Figure S2). After the rhizosphere soil was collected, the fine roots of the pine trees were cut with scissors sterilized with 75% alcohol and placed in a new 2 mL frozen tube. The 25 g bulk soil samples (10–30 cm) without vegetation were selected and mixed thoroughly from each plot. The bulk soil from four plots within one elevation was then mixed thoroughly. All samples were stored on dry ice. After transportation to the laboratory, the frozen tube with rhizosphere soil was stored at −80 °C. In addition, the fine roots were cleaned and sonicated for 10 min in an ultrasonic bath cleaner (Branson, CT, USA) [33]. The roots were surface disinfected for 5 min in 0.5% NaClO followed by 5 min in 75% CH3CH2OH and washed with sterile water. The washed water from the final washing was inoculated on tryptic soy agar (TSA) medium and potato dextrose agar (PDA) medium to verify successful surface sterilization.

2.2. Soil and Root Chemical Properties

In addition, the roots and loose soil around the roots were collected from the 8 pine trees in the abovementioned plots and transported to the laboratory. The soil and roots were dried at 80 °C to a constant weight. The samples were ground to powder by an MM400 ball mill (Retsch, Shanghai, China), and then a 0.2 mm sieve was used to filter the samples. Then, organic carbon, total nitrogen, total phosphorus and soil pH were measured. Briefly, the organic carbon of the soil and roots was measured by the K2Cr2O7-H2SO4 volumetric method [34]. Total nitrogen transformed as NH4+ was analyzed using a Smartchem200 after digestion by concentrated H2O2-H2SO4, and the total phosphorus content was also measured by a SmartChem200 automatic chemical analyzer (AMS-Westco, Guidonia, Italy) [35,36]. The pH value of the soil was measured by a pHS-2F pH meter (INESA Scientific Instrument Co., Ltd., Shanghai, China).

2.3. DNA Isolation and Sequencing

For high-throughput sequencing, the total DNA of 200 mg of sterilized fine roots was extracted using the CTAB method for each root sample [37], and 0.5 g of soil was extracted with a commercial DNA extraction kit for each rhizosphere soil sample (FastDNA® SPIN Kit for Soil, MpBio, Shanghai, China). DNA quality was monitored by 0.8% agarose gel electrophoresis, and extracted DNA was diluted to a concentration of 1 ng/μL and stored at −20 °C until further processing. The diluted DNA was used as a template for PCR amplification. For bacterial diversity analysis, the V5-V7 variable regions of 16S rRNA genes were amplified with nested PCR using the first and second pair of PCR primers 799F-1392R and 799F-1193R, respectively [38,39]. For fungal diversity analysis, the ITS1 region was amplified with the universal primers ITS1F-ITS2R [40]. Twenty-microliter PCRs were run containing 4 μL 5×FastPfu Buffer, 2 μL 2.5 mM dNTPs, 0.8 μL forward primer (5 μM), 0.8 μL reverse primer (5 μM), 0.4 μL FastPfu Polymerase, 0.2 μL BSA, and 10 ng template DNA, and molecular-grade water was added to 20 μL. The V5-V7 variable regions of the 16S rRNA gene reaction were assessed in two rounds: the first round consisted of 3 min of initial denaturation at 95 °C, 27 cycles of 30 s at 95 °C, 30 s at 55 °C and 45 s at 72 °C, with a final 10 min elongation at 72 °C, and the second round consisted of 3 min of initial denaturation at 95 °C, 13 cycles of 30 s at 95 °C, 30 s at 55 °C and 45 s at 72 °C, with a final 10 min elongation at 72 °C. The ITS1 region of fungi was subjected to 3 min of initial denaturation at 95 °C, 30 cycles of 30 s at 95 °C, 30 s at 55 °C and 45 s at 72 °C, with a final 10 min elongation at 72 °C. Then, library construction and sequencing were performed using the Illumina MiSeq PE250 platform by Shanghai Majorbio Biopharm Technology Co., Ltd. (Shanghai, China). Raw sequences have been deposited in the Sequence Read Archive under Bioproject PRJNA870036.

2.4. Bioinformatic Analysis

Raw sequencing data were in the FASTQ format. Paired-end reads were quality-filtered by Trimmomatic and merged by FLASH software v1.2.11 [41]. We also removed low-quality sequences with average quality scores below 20 using a 50-bp sliding window trimming approach. The merging parameters were 10 bp minimal overlapping, 200 bp maximum overlapping and a 20% maximum mismatch rate. Sequences of each sample were separated according to barcodes (exactly matching), and reads containing ambiguous bases were removed. Then, reads with chimeras were detected and removed. These bioinformatic analyses were conducted using Quantitative Insights Into Microbial Ecology (QIIME v1.9.1) pipeline [42]. Clean reads were subjected to primer sequence removal and clustered to generate OTUs using UPARSE software v11 with a 97% similarity cutoff [43]. The representative read of each OTU was selected using the QIIME package. All representative reads were taxonomically identified against the Silva (Version 138) and Unite (Version 8.0) databases for bacteria and fungi using the RDP classifier (confidence threshold of 70%).

2.5. Data and Statistical Analyses

All reads obtained from 119 samples (8 rhizosphere soils, 8 root samples and one bulk soil sample from one elevation × 7 elevations) were divided into 25,705 bacterial OTUs and 7018 fungal OTUs. We then subsampled the remaining 45,860 bacterial reads and 96,322 fungal reads (minimized read sample) using MOTHUR v1.35.1 “sub.sample”. All data were analyzed according to data descriptions based on subsampled data. The rarefaction curve for each sample was calculated using MOTHUR v1.35.1 “rarefaction.single” and plotted using GraphPad Prism 9 (GraphPad Software, Inc., San Diego, CA, USA). Raincloud plots were generated according to Allen et al., (2019) using R v3.4.3 to investigate the variations in the morphological features with elevational gradients [44]. Linear regression was performed using the R v3.4.3 “stats” package to investigate the variation in the relative abundance of genera and the soil properties and plant nutrient content across the elevational gradient [45]. Nonmetric multidimensional scaling (NMDS) based on Bray–Curtis dissimilarity was performed using the R v3.4.3 “vegan” package to investigate the differences among groups [46]. Analysis of similarities (ANOSIM) was used to test the statistically significant differences based on OTU richness using the R package “vegan” [46]. Alpha diversity was calculated using the R “vegan” package and was plotted using GraphPad Prism 9 [46]. Additionally, the Bray–Curtis dissimilarities of samples among elevations and at the same elevation were calculated using the R “vegan” package [46]. The Kruskal–Wallis test (for three and more groups) and the Mann–Whitney U test (for two groups) were performed using the R v3.4.3 “stats” package to investigate the significant differences among groups [45]. Functional prediction for fungi and bacteria was achieved using PICRUSt2 [47]. Other diagrams were plotted in GraphPad Prism 9.

3. Results

3.1. Morphological Features

First, the DBH and height of 48 trees at each elevational site were measured. At 2600 m and 2900 m, there were two peaks for DBH and tree height (Figure 1). This revealed that 2600 m and 2900 m are the optimum elevations for the growth of pine trees. The DBH and tree height at 2300 m, 2500 m, 3100 m and 3300 m were significantly lower than those at 2600 m to 2900 m (p = 0.000, Mann–Whitney U test, Figure 1). The greatest DBH and tree height occurred at 2900 m.
Needle morphological features show relative species stability and can be used to identify coniferous species [48]. The ratios of the three needle clusters from 2300 m to 2700 m were significantly higher than those from 2900 m to 3300 m (p = 0.000, Mann–Whitney U test, Figure 1). The ratios of the three-needle clusters sharply decreased from 2700 m to 2900 m, indicating that the needle morphology switched from P. yunnanensis-like to P. densata-like (Supplementary Figure S3). Other morphological features were consistent with the switch from the P. yunnanensis-like type to the P. densata-like type. For example, needle length was longest at 2600 m and then decreased with elevation, and needle diameter and needle cluster diameter increased with elevation (Supplementary Figure S4).

3.2. Soil Chemical Properties and Plant Nutrient Contents

Linear regression was used to investigate the relationship between soil characteristics and the root nutrient contents along the elevational gradient (Figure 2). The organic carbon content of the soil and roots decreased with increasing elevation, and the organic carbon content of the roots had significant negative relationships with elevation (p < 0.05). There was no significant difference in the total nitrogen content of roots at different elevations, but there was a significant positive correlation between soil total nitrogen and elevation (p < 0.05). The total phosphorus content of the soil increased with increasing elevation, whereas the total phosphorus content of the roots decreased with increasing elevation. The total phosphorus contents of the soil and roots were significantly related to elevation (p < 0.05). Finally, the pH value had a significant positive correlation with elevation (p < 0.05).

3.3. Soil Chemical Properties and Plant Nutrient Contents

In total, 119 samples from seven elevations were sequenced and all samples were first subsampled to the minimized read sample, i.e., 96,322 reads for fungi and 45,860 reads for bacteria. The rarefaction curve reached saturation at the minimized number of reads (Supplementary Figure S5).
For fungi, there were two dominant phyla, i.e., Ascomycota (48.87%) and Basidiomycota (47.02%). Dominant genera included Geminibasidium (9.41%), Sebacina (8.60%) and Russula (8.50%) (Supplementary Figure S6). For bacteria, three known phyla accounted for more than 8%, i.e., Proteobacteria (41.58%), Actinobacteria (36.57%) and Firmicutes (8.15%). Thirteen genera accounted for more than 1% and the top five known genera were Mycobacterium (10.41%), Bradyrhizobium (8.97%), Candidatus Phytoplasma (4.67%), Rhodoplanes (1.99%) and Acidibacter (1.80%) (Supplementary Figure S6).

3.4. Differentiation of the Pine Root Microbiome at Different Elevations

According to the sequencing results, Ascomycota and Basidiomycota were the predominant phyla in roots, accounting for 58.95% and 39.08%, respectively (Supplementary Figure S6). There were five genera accounting for more than 5%, i.e., unclassified genera from Helotiales (15.60%), Russula (8.46%), Sebacina (8.44%) and unclassified genera from Hysteriales (7.72%). Additionally, Ascomycota and Basidiomycota were the predominant phyla in rhizosphere soil, accounting for 39.70% and 54.32%, respectively (Supplementary Figure S6). Geminibasidium (17.51%), Sebacina (8.77%), Russula (8.66%), Penicillium (6.53%), Oidiodendron (5.82%) and Pseudeurotium (5.36%) were the predominant genera.
For bacteria, Proteobacteria (40.19%), Actinobacteriota (37.53%) and Firmicutes (14.16%) were the predominant phyla in roots. Mycobacterium (11.33%), Candidatus Phytoplasma (9.92%) and Bradyrhizobium (9.92%) were the predominant genera. Proteobacteria and Actinobacteriota were the predominant phyla in roots, accounting for 42.74% and 35.68%, respectively (Supplementary Figure S6). There were three genera accounting for more than 5%, i.e., Bradyrhizobium (9.91%), Mycobacterium (9.58%) and an unclassified genus from Xanthobacteraceae (8.53%). In general, the variations in microbes in roots and rhizosphere soil were distinct at different elevations.
PCoA was used to evaluate the microbial communities recruited across the elevational gradient. Both rhizosphere soil and root microbial communities were significantly different along the elevational gradient (Figure 3, p < 0.05). The distribution of bulk soil groups on PC1 was wide for both fungi and bacteria (Figure 3, Supplementary Figure S7). The degree of overlap of bacterial microbial communities was higher than that of the fungal microbial community in both rhizosphere soil and root endophytes (Figure 3). The elevational changes in microbial communities had similar characteristics; that is, the microbial groups at a certain elevation overlapped more with those at a similar elevation. However, there were a few exceptions; for example, the root bacterial community at 2300 m overlapped more with that at high elevations (3100 m and 3300 m).
Furthermore, we determined the linear regression between elevation and the dominant genera (>1.0%) in roots and soil. There were 15 bacterial genera that had significant variation among the elevational sites. Mycobacterium and two other genera in roots, Burkholderia-Caballeronia-Paraburkholderia and six other genera in rhizosphere soil decreased with elevation, whereas unclassified Xanthobacteraceae, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium in roots, Mycobacterium and two other genera in rhizosphere soil increased with elevation (Figure 4). Similarly, 11 fungal genera had significant variation among the elevational sites (Figure 4). There was partial consistency between OTUs and genera. For example, bacterial OTU1587 (Mycobacterium) and OTU9758 (Rhodoplanes) in roots decreased with elevation, whereas OTU13052 (Mycobacterium) in rhizosphere soil increased with elevation (Supplementary Figure S8).

3.5. Strategies for the Differential Recruitment of Root Endophytes by Related Pines

According to morphological features, the pines were divided into two types: the P. yunnanensis-like type (2300–2700 m) and the P. densata-like type (2900–3300 m). The endophytic fungal and bacterial communities of these two types were significantly different (Figure 5a,b, p = 0.001), indicating different recruited microbiome strategies between the two types.
Then, we further analyzed the different microbes recruited by the two types. The P. yunnanensis-like type recruited more Mucoromycota and Ascomycota: Hysteriales, Niesslia, Herpotrichiellaceae, Archaeorhizomyces; Basidiomycota: Helvellosebacina, etc. Meanwhile, the P. yunnanensis-like type recruited more Actinobacteriota: Actinophytocola, Pseudonocardia, Solirubrobacter, etc. (Figure 5c–f). However, the P. densata-like type recruited more Ascomycota: Dothideomycetes, Ilyonectria; Basidiomycota: Hydropus, Mycenella, as well as Proteobacteria: Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade, Hyphomicrobium, Rhodomicrobium, Rhizobacter, Steroidobacter; Firmicutes: Paenibacillus, etc. In general, different pine trees had different recruitment strategies regarding the microbial communities, and the species composition and quantity of microbes varied greatly (Figure 5c–f).
Comparative analysis of bacterial KEGG pathways from samples of two types and carbohydrate metabolism (e.g., propanoate metabolism, glycolysis/gluconeogenesis, and starch and sucrose metabolism) and lipid metabolism were enriched in the P. yunnanensis-like type, and environmental adaptation, cellular community prokaryotes (e.g., quorum sensing), vitamins and signal transduction (e.g., two-component system) and metabolism of cofactors and vitamins (e.g., porphyrin and chlorophyll metabolism, pantothenate and CoA biosynthesis, folate biosynthesis) were enriched in the P. densata-like type (Figure 6a, Supplementary Figure S9). For fungal MetaCyc pathways, the ubiquinol biosynthesis and phosphatidylglycerol biosynthesis were enriched in P. yunnanensis-like type, and octane oxidation was enriched in the P. densata-like type (Figure 6b).

3.6. Pines Recruited More Diverse Microbial Communities from Similar Soil Microbial Communities

To explore the origin of root endophytes, the alpha and beta diversities of the root and rhizosphere soil microbial communities were measured. First, the rarefaction curve showed that the species number in rhizosphere soils was higher than that in roots (Supplementary Figure S5). Similarly, the alpha diversity of rhizosphere soils was significantly higher than that of roots (p < 0.001, Mann–Whitney U test). Moreover, we verified that the microbial communities were completely divided into root and rhizosphere soil groups in both fungi and bacteria (Figure 7, p = 0.001 for Bray–Curtis dissimilarity), explained primarily by isolation sources (roots vs. rhizosphere soils) rather than by elevation sites, although there were differences in soil characteristics and climate among the elevation sampling sites.
We inferred that these pines might enrich specific microbial communities from rhizosphere soils and cause a significant transformation of the endosphere microbial community. Therefore, the proportion of root endophytes enriched in rhizosphere soil was measured. The results showed that 70% of the fungi in the roots accounted for only 9.66% of the corresponding OTUs in the rhizosphere soil. Similarly, 70% of the bacteria in the roots accounted for 38.90% of the corresponding OTUs in the rhizosphere soil (Figure 8a,b). Additionally, the different microbes at the phylum and genus levels were determined between the root and soil groups. The fungi switched from Basidiomycota predominant in the rhizosphere to Ascomycota predominant in the root endosphere, caused by Helotiales, Hysteriales, Venturia, Phialocephala and Sordariomycetes, belonging to Ascomycota, which were enriched in roots from the soil microbiome pool (Figure 8c–f). The bacteria were switched from Acidobacteriota, Myxococcota and Chloroflexi predominant in the rhizosphere to Firmicutes predominant in the root endosphere, caused by Firmicutes: Candidatus Phytoplasma enriched in roots from the soil microbiome pool. Additionally, the genera Proteobacteria: Acidibacter, Actinobacteriota: Acidothermus, Actinobacteriota: Actinophytocola and Firmicutes: Streptococcus were enriched in roots (Figure 8c–f).
Comparative analysis of bacterial KEGG pathways from samples of root and rhizosphere soil showed that carbohydrate metabolism (e.g., pyruvate metabolism, glycolysis/gluconeogenesis and propanoate metabolism), energy metabolism (e.g., methane metabolism) and lipid metabolism were enriched in roots, while the metabolism of cofactors and vitamins, signal transduction (e.g., two-component system) and cellular community prokaryotes were enriched in rhizosphere soil (Figure 9a, Supplementary Figure S10). For fungal MetaCyc pathways, the pyrimidine deoxyribonucleotides, superpathway of adenosine nucleotides and palmitate biosynthesis were enriched in rhizosphere soil and aerobic respiration, TCA cycle II et al. were enriched in roots (Figure 9b).
Based on the PCoA results, we found that the microbial communities in the rhizosphere soil were more similar than those in the roots (Figure 7). To quantify this, the distance based on Bray–Curtis dissimilarity among elevations and at specific elevations was measured. The results showed that the dissimilarities of endophytes were significantly higher than those of rhizosphere soil in both fungal and bacterial communities based on all elevational samples (Figure 10a,c, p < 0.001). Additionally, the dissimilarities in the microbial community among elevations were higher than those at a particular elevation, indicating possible differences in microbial communities among elevations and species (Figure 10b,d, p < 0.001). Compared with fungal communities, bacterial communities were more similar in both roots (bacteria:fungidissimilarity = 0.734:0.883) and rhizosphere soil (bacteria:fungidissimilarity = 0.539:0.807) (Figure 8).

4. Discussion

Pinus densata is considered a homoploid hybrid speciation (HHS) type species that originated from the putative parent species P. tabuliformis and P. yunnanensis [14]. The intermediate morphology of pine hybrids is contributed by both parental species, and hybrid derivatives are more similar to the parent with higher genetic contribution, resulting in cryptic introgression [49]. Xing et al., (2014) reported that in mixed zones, needle morphological features show relative species stability and can be used to identify coniferous species, and the ratios of three-needle clusters were 46.67% and 26.09% in P. yunnanensis and P. densata, respectively [48]. In this study, we also found significant morphological changes, especially a change from three-needle clusters to two-needle clusters (Figure 1). The ratio of the three-needle clusters changed from 83.75% (2300–2700 m) to 34.58% (2900–3300 m) on average, showing the change from the P. yunnanensis-like type to the P. densata-like type. Additionally, the decrease in needle length and increase in needle diameter from 2300 m to 3300 m indicated the more efficient water use of P. densata-like types. This drought tolerance trait would likely promote its ecological separation from its parental species and benefit the high-elevation adaptability of two-needle pines [50].
Interestingly, we found that the transition of three-needle pines to two-needle pines occurred very quickly, from 2700 m to 2900 m, with the ratio of three-needle clusters decreasing from 85.0% to 37.5%, whereas we previously thought the conversion might be gradual and slow. In other words, a pine tree quickly completes the transition from three needles to two needles, thus efficiently completing its adaptation to the environment. In addition, pine trees at 2600 m to 2900 m had greater DBHs and tree heights, which also shows the effectiveness of this rapid transition. The sampling sites in a few studies also showed a rapid switch between P. yunnanensis and P. densata in the heart of the Hengduan Mountains [23,24]. In particular, pines at 2900 m had the greatest tree height and DBH, revealing that two-needle pines were better adapted to this mixed zone.
It is now widely accepted that the establishment of plant-associated microbial communities is not random but is controlled by specific assembly rules [51]. Exogenous driving factors, such as soil characteristics, soil quality [52,53], seasons [54,55] and climate, are characterized by changing the microbial pool to change the microbial composition of plant roots. Endogenous drivers, including the plant host type [39,56], plant parts [57,58], plant development stage [59] and plant immunity [60], are characterized by the process of host-microbe selection in a specific soil microbial pool. While the soil microbial pool is the result of exogenous factors, the bacterial and fungal communities of plant roots (both rhizosphere and endophytic microbes) are subsets of the microbial community in the soil [58,61]. Unlike a study at a larger geographic scale [62], in this study, we limited the geographic variation of the microbial pool by focusing our study on a single mountain. Thus, the differences in root endophytic microbes should be mainly due to the microbe recruitment strategies of different pines and their adaptation to the microenvironment, rather than due to the differences in the soil microbial pool in the geographical environment. For the selection of sample plots, Mao and Wang established the distribution model of P. densata and its parents by combining extensive field investigations and geographic information system-based analyses, and the results show that environmental adaptation and geographic isolation can maintain and strengthen the differentiation of species. At the same time, it is pointed out that the large environmental heterogeneity also leads to the difficulty of quantitative assessment on the geographical scale [23]. In this study, it was easy to find a large area of pure P. densata forest in the sampling area, but it was difficult to find a mountain ecosystem with the characteristics of a mixed zone and a 1000 m elevation difference. Therefore, this paper only discusses one mountain ecosystem, and the results will have some reference value.
Although there are many bacterial groups in nature, the major bacterial groups and their abundance distribution are similar in bulk soil and rhizosphere soil at the phylum level, i.e., Proteobacteria, Actinobacteria and Acidobacteria. The root endophytic bacteria were mainly composed of Proteobacteria and Actinobacteria. However, the abundance of Firmicutes in roots was approximately five times that in rhizosphere soil, which indicated that there were more Firmicutes in roots as endophytic bacteria, similar to the findings reported by Trivedi et al., (2020) [63]. Compared with bacteria, other root endophytic fungi also play important roles and have the ecological function of helping plants absorb phosphorus [64,65,66]. The fungi that colonize plant roots are mainly Ascomycota and Basidiomycota [63]. Functionally, endophytic bacteria are already in the host plant, mainly performing metabolism and immune-related functions [67], while rhizosphere soil bacteria act as connectors between the rhizosphere and the external environment. The functions performed are mainly related to external communication and the exchange of substances (molecular communication in the rhizosphere). Both rhizosphere and root endophytic fungi were related to energy metabolism and environmental adaptation.
Similar to most studies, the microbial community structure in the rhizosphere and roots of plants showed distinct differences at different elevations (Figure 3), suggesting that elevation is an important driver of microbial community structure [2,29,68]. Some beneficial microorganisms, such as the drought-tolerant indicator bacterial group Rhodoplanes [69], Bradyrhizobium and others, decreased with elevation, suggesting a gradual deterioration of environmental conditions. Similarly, the fungus Oidiodendron, as a group of ericoid mycorrhizal fungi that can increase plant yield and drought resistance, decreased with elevation [70]. Overall, the effects of the environment and host plants on microorganisms exhibit complex variations.
In this study, we also measured the soil chemical content and nutrient elements in roots and found that root organic carbon, total phosphorus, soil total nitrogen, total phosphorus and pH showed a strong correlation with the elevational gradient (Supplementary Figure S11). The positive correlation between the soil nitrogen content and elevation was consistent with that reported in a previous study and may be related to land cover transformation [71]. Then, we further studied the effects of soil properties and plant nutrient contents on the microbial community. For the driving force of the soil microbial community structure, soil organic carbon, total nitrogen, total phosphorus and pH had significant effects, among which nitrogen and pH were the most important factors affecting the change in soil microbial communities with elevation (Supplementary Figure S12) [5,6,7,72]. For the distribution of the rhizosphere soil and root endophytic microbiome at elevation, the organic carbon, total nitrogen and total phosphorus contents of soil and roots were the main driving factors, which indicated that elevation affected both biotic and abiotic factors [2,3], eventually affecting the recruitment of microbes to pine trees (Supplementary Figure S11).
We divided all samples into root samples and soil samples for analysis. Additionally, we divided the root endophytes into P. yunnanensis-like types and P. densata-like types according to their morphological features, and then the characteristics of the microbial community and recruitment strategy were analyzed. Interestingly, although the microbial alpha diversity of soil was significantly higher than that of root endophytes, the differentiation of root endophyte communities in different samples was significantly greater than that of the soil microbiome, revealing that different pine trees recruited different microbiomes from similar microbiome pools in rhizosphere soil to adapt quickly to the environment (Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10). Similarly, a previous study also showed that the endophytic microbial community of roots exhibited strong correspondence with the host plant phylogeny, but this was not the case in the rhizosphere [73]. This also indicates that the root system selected a different microbial community from the rhizosphere soil microbes to adapt to the environment. The result of this recruitment was that 70% of the fungi in the roots accounted for only 9.66% of the corresponding OTUs in the rhizosphere soil. Similarly, 70% of the bacteria in the roots accounted for 38.90% of the corresponding OTUs in the rhizosphere soil. These results suggested that the recruitment of the root endophytes is not random and that the “host might have selected” more different microbes even more than the elevational difference [33]. The P. yunnanensis-like type recruited more Mucoromycota and Actinobacteriota. Meanwhile, the P. densata-like type recruited more Proteobacteria. For the function of bacteria, the root endophytic bacteria enriched in the P. densata-like type have similar functions to the rhizosphere soil microorganisms mentioned above, mainly by environmental adaptation, quorum sensing and the metabolism of auxiliary factors and vitamins. It is possible that the metabolism related to the environmental adaptation function is more active. The endophytes in the roots of the P. yunnanensis-like type were mainly involved in carbohydrate and lipid metabolism. In terms of fungal function, root endophytic fungi of P. yunnanensis-like type were related to energy metabolism and fungi of P. densata-like type were related to utilization of n-alkanes. Overall, the construction of the pine root microbiome is a complex coevolution and interaction between the host and microbes [74].

5. Conclusions

To the best of our knowledge, this is one of the few studies that covered the elevational changes in microbial community recruitment strategies across homoploid hybrid species [14]. Although it is largely unknown how plant evolution has shaped root microbial communities and, in turn, how these microbes affect plant ecology, such as the ability to mitigate biotic and abiotic stressors [73], this study provides insights into environmental adaptive strategies during species evolution. In brief, this paper studied the climatic adaptation strategies of the hybrid species in the mixed zone of P. yunnanensis and P. densata in terms of morphology and chemical and microbial recruitment. The results showed that the hybrid species rapidly changed the morphology of the needles and recruited different microbiomes from similar soil microbial pools in a small elevation range to adapt to biotic and abiotic influences driven by elevation. This study provides new insights and perspectives for understanding the mechanism of homoploid hybridization and the adaptation mechanism of species in coniferous forests on mountain plateaus.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14040685/s1, Figure S1: workflow of sample collection; Figure S2: sampling sites; Figure S3: morphological features of pine needles at different elevations; Figure S4: raincloud plot showing needle morphological features at different elevations; Figure S5: rarefaction curves for different groups of samples; Figure S6: composition and relative abundance of microbial communities at the phylum and genus levels; Figure S7: the distribution of samples at different elevations on the PC1 axis analysed by PCoA; Figure S8: variation in OTU abundance with altitudinal gradient; Figure S9: the different groups between the two types of pines at the OTU level and functions of bacteria at the KEGG 2 level; Figure S10: the different groups between rhizosphere soil and roots at the OTU level and functions of bacteria at the KEGG 2 level; Figure S11: RDA/CCA of the microbial communities of different pine types; Figure S12: RDA/CCA of the microbial communities of rhizosphere soil and roots; Table S1: sampling site information; Table S2: Plant information for sampling sites at different elevations.

Author Contributions

Conceptualization, J.T. and L.C.; methodology, Y.X. and L.C.; investigation, D.M., J.T., N.C., Z.D., Y.Y. and D.Y.; writing—original draft preparation, D.M., N.C., S.C., Y.H. and L.C.; writing—review and editing, D.M., Y.H., Y.X. and L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Nature Science Foundation of China (NSFC), grant number 32160321; Applied Basic Research Programs of Science and Technology Department of Yunnan Province, grant number 202201AT070023; Yunnan Revitalization Talent Support Program Young Talent Project, grant number XDYC-QNRC-2022-0250; Southwest Forestry University Research Project, grant number 112113; and Research Innovation Fund for Graduate Students of Yunnan Provincial Department of Education, grant number 2022Y586.

Data Availability Statement

Raw sequences have been deposited in the Sequence Read Archive under Bioproject PRJNA870036.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Merino-Martín, L.; Hernández-Cáceres, D.; Reverchon, F.; Angeles-Alvarez, G.; Zhang, G.; Dunoyer de Segonzac, D.; Dezette, D.; Stokes, A. Habitat partitioning of soil microbial communities along an elevation gradient: From plant root to landscape scale. Oikos 2023, 2023, e09034. [Google Scholar] [CrossRef]
  2. Zuo, Y.W.; He, P.; Zhang, J.H.; Li, W.Q.; Ning, D.H.; Zeng, Y.L.; Yang, Y.; Xia, C.Y.; Zhang, H.; Deng, H.P. Contrasting responses of multispatial soil fungal communities of Thuja sutchuenensis Franch., an extremely endangered conifer in Southwestern China. Microbiol. Spectr. 2022, 10, e0026022. [Google Scholar] [CrossRef] [PubMed]
  3. Tang, M.; Li, L.; Wang, X.; You, J.; Li, J.; Chen, X. Elevational is the main factor controlling the soil microbial community structure in alpine tundra of the Changbai Mountain. Sci. Rep. 2020, 10, 12442. [Google Scholar] [CrossRef] [PubMed]
  4. Wang, J.; Hu, A.; Meng, F.; Zhao, W.; Yang, Y.; Soininen, J.; Shen, J.; Zhou, J. Embracing mountain microbiome and ecosystem functions under global change. New Phytol. 2022, 234, 1987–2002. [Google Scholar] [CrossRef]
  5. Shen, C.; Gunina, A.; Luo, Y.; Wang, J.; He, J.Z.; Kuzyakov, Y.; Hemp, A.; Classen, A.T.; Ge, Y. Contrasting patterns and drivers of soil bacterial and fungal diversity across a mountain gradient. Environ. Microbiol. 2020, 22, 3287–3301. [Google Scholar] [CrossRef] [PubMed]
  6. Liu, X.; Yang, T.; Shi, Y.; Zhu, Y.; He, M.; Zhao, Y.; Adams, J.M.; Chu, H. Strong partitioning of soil bacterial community composition and co-occurrence networks along a small-scale elevational gradient on Zijin Mountain. Soil Ecol. Lett. 2021, 3, 290–302. [Google Scholar] [CrossRef]
  7. Praeg, N.; Seeber, J.; Leitinger, G.; Tasser, E.; Newesely, C.; Tappeiner, U.; Illmer, P. The role of land management and elevation in shaping soil microbial communities: Insights from the Central European Alps. Soil Biol. Biochem. 2020, 150, 107951. [Google Scholar] [CrossRef]
  8. Odriozola, I.; Navrátilová, D.; Tláskalová, P.; Klinerová, T.; Červenková, Z.; Kohout, P.; Větrovský, T.; Čížková, P.; Starý, M.; Baldrian, P. Predictors of soil fungal biomass and community composition in temperate mountainous forests in Central Europe. Soil Biol. Biochem. 2021, 161, 108366. [Google Scholar] [CrossRef]
  9. Puissant, J.; Cécillon, L.; Mills, R.T.E.; Robroek, B.J.M.; Gavazov, K.; De Danieli, S.; Spiegelberger, T.; Buttler, A.; Brun, J.-J. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biol. Biochem. 2015, 80, 296–305. [Google Scholar] [CrossRef] [Green Version]
  10. Cai, Z.; Wang, X.; Bhadra, S.; Gao, Q. Distinct factors drive the assembly of quinoa-associated microbiomes along elevation. Plant Soil. 2020, 448, 55–69. [Google Scholar] [CrossRef]
  11. Wu, S.; Wang, Y.; Wang, Z.; Shrestha, N.; Liu, J. Species divergence with gene flow and hybrid speciation on the Qinghai-Tibet Plateau. New Phytol. 2022, 234, 392–404. [Google Scholar] [CrossRef] [PubMed]
  12. Armenise, L.; Simeone, M.C.; Piredda, R.; Schirone, B. Validation of DNA barcoding as an efficient tool for taxon identification and detection of species diversity in Italian conifers. Eur. J. Forest. Res. 2012, 131, 1337–1353. [Google Scholar] [CrossRef]
  13. Jin, W.T.; Gernandt, D.S.; Wehenkel, C.; Xia, X.M.; Wei, X.X.; Wang, X.Q. Phylogenomic and ecological analyses reveal the spatiotemporal evolution of global pines. Proc. Natl. Acad. Sci. USA 2021, 118, e2022302118. [Google Scholar] [CrossRef]
  14. Meng, J.; Mao, J.F.; Zhao, W.; Xing, F.; Chen, X.; Liu, H.; Xing, Z.; Wang, X.R.; Li, Y. Adaptive differentiation in seedling traits in a hybrid pine species complex, Pinus densata and its parental species, on the Tibetan plateau. PLoS ONE 2015, 10, e0118501. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Wang, X.R.; Szmidt, A.E. Hybridization and chloroplast dna variation in a pinus species complex from Asia. Evolution 1994, 48, 1020–1031. [Google Scholar] [CrossRef] [PubMed]
  16. Wang, X.R.; Szmidt, A.E.; Savolainen, O. Genetic composition and diploid hybrid speciation of a high mountain pine, Pinus densata, native to the Tibetan plateau. Genetics 2001, 159, 337–346. [Google Scholar] [CrossRef] [PubMed]
  17. Wang, X.R.; Szmidt, A.E.; Lewandowski, A.; Wang, Z.R. Evolutionary analysis of Pinus densata Masters, a putative Tertiary hybrid: 1. Allozyme variation. Theor. Appl. Genet. 1990, 80, 635–640. [Google Scholar] [CrossRef] [PubMed]
  18. Yu, H.; Ge, S.; Hong, D.Y. Allozyme diversity and population genetic structure of Pinus densata master in Northwestern Yunnan, China. Biochem. Genet. 2000, 38, 139–147. [Google Scholar] [CrossRef] [PubMed]
  19. Liu, Z.L.; Zhang, D.; Hong, D.Y.; Wang, X.R. Chromosomal localization of 5S and 18S-5.8S-25S ribosomal DNA sites in five Asian pines using fluorescence in situ hybridization. Theor. Appl. Genet. 2003, 106, 198–204. [Google Scholar] [CrossRef]
  20. Song, B.H.; Wang, X.Q.; Wang, X.R.; Ding, K.Y.; Hong, D.Y. Cytoplasmic composition in Pinus densata and population establishment of the diploid hybrid pine. Mol. Ecol. 2003, 12, 2995–3001. [Google Scholar] [CrossRef] [Green Version]
  21. Song, B.H.; Wang, X.Q.; Wang, X.R.; Sun, L.J.; Hong, D.Y.; Peng, P.H. Maternal lineages of Pinus densata, a diploid hybrid. Mol. Ecol. 2002, 11, 1057–1063. [Google Scholar] [CrossRef] [PubMed]
  22. Fort, M. The Himalayas: From mountain building to landform evolution in a changing world. Geogr. Pol. 2011, 84, 15–37. [Google Scholar] [CrossRef] [Green Version]
  23. Mao, J.F.; Wang, X.R. Distinct niche divergence characterizes the homoploid hybrid speciation of Pinus densata on the Tibetan plateau. Am. Nat. 2011, 177, 424–439. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Zhao, W.; Meng, J.; Wang, B.; Zhang, L.; Xu, Y.; Zeng, Q.Y.; Li, Y.; Mao, J.F.; Wang, X.R. Weak crossability barrier but strong juvenile selection supports ecological speciation of the hybrid pine Pinus densata on the Tibetan plateau. Evolution. 2014, 68, 3120–3133. [Google Scholar] [CrossRef] [Green Version]
  25. Liu, Y.; Gao, J.; Bai, Z.; Wu, S.; Li, X.; Wang, N.; Du, X.; Fan, H.; Zhuang, G.; Bohu, T.; et al. Unraveling mechanisms and impact of microbial recruitment on oilseed rape (Brassica napus L.) and the rhizosphere mediated by plant growth-promoting rhizobacteria. Microorganisms 2021, 9, 161. [Google Scholar] [CrossRef]
  26. Zhou, J.; Chai, X.; Zhang, L.; George, T.S.; Wang, F.; Feng, G. Different arbuscular mycorrhizal fungi cocolonizing on a single plant root system recruit distinct microbiomes. mSystems 2020, 5, e00929-20. [Google Scholar] [CrossRef]
  27. Hartman, K.; Tringe, S.G. Interactions between plants and soil shaping the root microbiome under abiotic stress. Biochem. J. 2019, 476, 2705–2724. [Google Scholar] [CrossRef] [Green Version]
  28. Dilla-Ermita, C.J.; Lewis, R.W.; Sullivan, T.S.; Hulbert, S.H. Wheat genotype-specific recruitment of rhizosphere bacterial microbiota under controlled environments. Front. Plant Sci. 2021, 12, 718264. [Google Scholar] [CrossRef]
  29. Gong, S.; Feng, B.; Jian, S.P.; Wang, G.S.; Ge, Z.W.; Yang, Z.L. Elevation matters more than season in shaping the heterogeneity of soil and root associated ectomycorrhizal fungal community. Microbiol. Spectr. 2022, 10, e0195021. [Google Scholar] [CrossRef]
  30. Zi, H.; Jiang, Y.; Cheng, X.; Li, W.; Huang, X. Change of rhizospheric bacterial community of the ancient wild tea along elevational gradients in Ailao mountain, China. Sci. Rep. 2020, 10, 9203. [Google Scholar] [CrossRef]
  31. Park, K.H.; Yoo, S.; Park, M.S.; Kim, C.S.; Lim, Y.W. Different patterns of belowground fungal diversity along altitudinal gradients with respect to microhabitat and guild types. Environ. Microbiol. Rep. 2021, 13, 649–658. [Google Scholar] [CrossRef]
  32. Institute of Botany. Flora of China. The Chinese Academy of Sciences. 2019. Available online: http://www.iplant.cn/foc (accessed on 8 August 2022).
  33. Chen, L.; Fang, K.; Zhou, J.; Yang, Z.P.; Dong, X.F.; Dai, G.H.; Zhang, H.B. Enrichment of soil rare bacteria in root by an invasive plant Ageratina adenophora. Sci. Total Environ. 2019, 683, 202–209. [Google Scholar] [CrossRef]
  34. Yeomans, J.C.; Bremner, J.M. A rapid and precise method for routine determination of organic carbon in soil. Commun. Soil Sci. Plant Anal. 1988, 19, 1467–1476. [Google Scholar] [CrossRef]
  35. Dong, J.; Gruda, N.; Li, X.; Tang, Y.; Duan, Z. Impacts of elevated CO2 on nitrogen uptake of cucumber plants and nitrogen cycling in a greenhouse soil. Appl. Soil. Ecol. 2020, 145, 103342. [Google Scholar] [CrossRef]
  36. Liang, F.; Xu, L.; Ji, L.; He, Q.; Wu, L.; Yan, S. A new approach for biogas slurry disposal by adopting CO2-rich biogas slurry as the flower fertilizer of Spathiphyllum: Feasibility, cost and environmental pollution potential. Sci. Total Environ. 2021, 770, 145333. [Google Scholar] [CrossRef]
  37. Murray, M.G.; Thompson, W.F. Rapid isolation of high molecular weight plant DNA. Nucleic. Acids. Res. 1980, 8, 4321–4325. [Google Scholar] [CrossRef] [Green Version]
  38. Bulgarelli, D.; Garrido-Oter, R.; Münch, P.C.; Weiman, A.; Dröge, J.; Pan, Y.; McHardy, A.C.; Schulze-Lefert, P. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 2015, 17, 392–403. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Lundberg, D.S.; Lebeis, S.L.; Paredes, S.H.; Yourstone, S.; Gehring, J.; Malfatti, S.; Tremblay, J.; Engelbrektson, A.; Kunin, V.; Del Rio, T.G.; et al. Defining the core Arabidopsis thaliana root microbiome. Nature 2012, 488, 86–90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Adams, R.I.; Miletto, M.; Taylor, J.W.; Bruns, T.D. Dispersal in microbes: Fungi in indoor air are dominated by outdoor air and show dispersal limitation at short distances. ISME J. 2013, 7, 1262–1273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Reyon, D.; Tsai, S.Q.; Khayter, C.; Foden, J.A.; Sander, J.D.; Joung, J.K. FLASH assembly of TALENs for high-throughput genome editing. Nat. Biotechnol. 2012, 30, 460–465. [Google Scholar] [CrossRef]
  42. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  43. Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996–998. [Google Scholar] [CrossRef] [PubMed]
  44. Allen, M.; Poggiali, D.; Whitaker, K.; Marshall, T.R.; Kievit, R.A. Raincloud plots: A multi-platform tool for robust data visualization. Wellcome Open Res. 2019, 4, 63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021; Available online: https://www.R-project.org/ (accessed on 23 September 2022).
  46. Oksanen, J.; Simpson, G.L.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Minchin, P.R.; O’Hara, R.B.; Solymos, P.; Stevens, M.H.H.; Szoecs, E.; et al. Vegan: Community Ecology Package; R Package Version 2.6-2. Available online: https://github.com/vegandevs/vegan/ (accessed on 23 September 2022).
  47. Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 2020, 38, 685–688. [Google Scholar] [CrossRef] [PubMed]
  48. Xing, F.; Mao, J.F.; Meng, J.; Dai, J.; Zhao, W.; Liu, H.; Xing, Z.; Zhang, H.; Wang, X.R.; Li, Y. Needle morphological evidence of the homoploid hybrid origin of Pinus densata based on analysis of artificial hybrids and the putative parents, Pinus tabuliformis and Pinus yunnanensis. Ecol. Evol. 2014, 4, 1890–1902. [Google Scholar] [CrossRef]
  49. Buck, R.; Hyasat, S.; Hossfeld, A.; Flores-Rentería, L. Patterns of hybridization and cryptic introgression among one- and four-needled pinyon pines. Ann. Bot. 2020, 126, 401–411. [Google Scholar] [CrossRef]
  50. Ma, F.; Zhao, C.; Milne, R.; Ji, M.; Chen, L.; Liu, J. Enhanced drought-tolerance in the homoploid hybrid species Pinus densata: Implication for its habitat divergence from two progenitors. New Phytol. 2010, 185, 204–216. [Google Scholar] [CrossRef]
  51. Reinhold-Hurek, B.; Bünger, W.; Burbano, C.S.; Sabale, M.; Hurek, T. Roots shaping their microbiome: Global hotspots for microbial activity. Annu. Rev. Phytopathol. 2015, 53, 403–424. [Google Scholar] [CrossRef]
  52. Bulgarelli, D.; Rott, M.; Schlaeppi, K.; Ver Loren van Themaat, E.; Ahmadinejad, N.; Assenza, F.; Rauf, P.; Huettel, B.; Reinhardt, R.; Schmelzer, E.; et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 2012, 488, 91–95. [Google Scholar] [CrossRef]
  53. Gottel, N.R.; Castro, H.F.; Kerley, M.; Yang, Z.; Pelletier, D.A.; Podar, M.; Karpinets, T.; Uberbacher, E.; Tuskan, G.A.; Vilgalys, R.; et al. Distinct microbial communities within the endosphere and rhizosphere of Populus deltoides roots across contrasting soil types. Appl. Environ. Microb. 2011, 77, 5934–5944. [Google Scholar] [CrossRef] [Green Version]
  54. Dombrowski, N.; Schlaeppi, K.; Agler, M.T.; Hacquard, S.; Kemen, E.; Garrido-Oter, R.; Wunder, J.; Coupland, G.; Schulze-Lefert, P. Root microbiota dynamics of perennial Arabis alpina are dependent on soil residence time but independent of flowering time. ISME J. 2017, 11, 43–55. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Shakya, M.; Gottel, N.; Castro, H.; Yang, Z.K.; Gunter, L.; Labbé, J.; Muchero, W.; Bonito, G.; Vilgalys, R.; Tuskan, G.; et al. A multifactor analysis of fungal and bacterial community structure in the root microbiome of mature Populus deltoides trees. PLoS ONE 2013, 8, e76382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Bálint, M.; Tiffin, P.; Hallström, B.; O’Hara, R.B.; Olson, M.S.; Fankhauser, J.D.; Piepenbring, M.; Schmitt, I. Host genotype shapes the foliar fungal microbiome of balsam poplar (Populus balsamifera). PLoS ONE 2013, 8, e53987. [Google Scholar] [CrossRef] [Green Version]
  57. Bai, Y.; Müller, D.B.; Srinivas, G.; Garrido-Oter, R.; Potthoff, E.; Rott, M.; Dombrowski, N.; Münch, P.C.; Spaepen, S.; Remus-Emsermann, M.; et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 2015, 528, 364–369. [Google Scholar] [CrossRef] [PubMed]
  58. Coleman-Derr, D.; Desgarennes, D.; Fonseca-Garcia, C.; Gross, S.; Clingenpeel, S.; Woyke, T.; North, G.; Visel, A.; Partida-Martinez, L.P.; Tringe, S.G. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 2016, 209, 798–811. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Chaparro, J.M.; Badri, D.V.; Vivanco, J.M. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 2014, 8, 790–803. [Google Scholar] [CrossRef] [Green Version]
  60. Lebeis, S.L.; Paredes, S.H.; Lundberg, D.S.; Breakfield, N.; Gehring, J.; McDonald, M.; Malfatti, S.; Glavina del Rio, T.; Jones, C.D.; Tringe, S.G.; et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 2015, 349, 860–864. [Google Scholar] [CrossRef] [Green Version]
  61. Hamonts, K.; Trivedi, P.; Garg, A.; Janitz, C.; Grinyer, J.; Holford, P.; Botha, F.C.; Anderson, I.C.; Singh, B.K. Field study reveals core plant microbiota and relative importance of their drivers. Environ. Microbiol. 2018, 20, 124–140. [Google Scholar] [CrossRef]
  62. Huang, C.L.; Sarkar, R.; Hsu, T.W.; Yang, C.F.; Chien, C.H.; Chang, W.C.; Chiang, T.Y. Endophytic microbiome of biofuel plant Miscanthus sinensis (Poaceae) interacts with environmental gradients. Microb. Ecol. 2020, 80, 133–144. [Google Scholar] [CrossRef]
  63. Trivedi, P.; Leach, J.E.; Tringe, S.G.; Sa, T.; Singh, B.K. Plant–microbiome interactions: From community assembly to plant health. Nat. Rev. Microbiol. 2020, 18, 607–621. [Google Scholar] [CrossRef]
  64. Bonito, G.; Reynolds, H.; Robeson II, M.S.; Nelson, J.; Hodkinson, B.P.; Tuskan, G.; Schadt, C.W.; Vilgalys, R. Plant host and soil origin influence fungal and bacterial assemblages in the roots of woody plants. Mol. Ecol. 2014, 23, 3356–3370. [Google Scholar] [CrossRef] [PubMed]
  65. Hiruma, K.; Gerlach, N.; Sacristán, S.; Nakano, R.T.; Hacquard, S.; Kracher, B.; Neumann, U.; Ramírez, D.; Bucher, M.; O’Connell Richard, J.; et al. Root endophyte Colletotrichum tofieldiae confers plant fitness benefits that are phosphate status dependent. Cell 2016, 165, 464–474. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Almario, J.; Jeena, G.; Wunder, J.; Langen, G.; Zuccaro, A.; Coupland, G.; Bucher, M. Root-associated fungal microbiota of nonmycorrhizal Arabis alpina and its contribution to plant phosphorus nutrition. Proc. Natl. Acad. Sci. USA 2017, 114, e9403–e9412. [Google Scholar] [CrossRef] [Green Version]
  67. Teixeira, P.J.P.; Colaianni, N.R.; Fitzpatrick, C.R.; Dangl, J.L. Beyond pathogens: Microbiota interactions with the plant immune system. Curr. Opin. Microbiol. 2019, 49, 7–17. [Google Scholar] [CrossRef]
  68. Wallace, J.; Laforest-Lapointe, I.; Kembel, S.W. Variation in the leaf and root microbiome of sugar maple (Acer saccharum) at an elevational range limit. PeerJ 2018, 6, e5293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. de Vries, F.T.; Griffiths, R.I.; Bailey, M.; Craig, H.; Girlanda, M.; Gweon, H.S.; Hallin, S.; Kaisermann, A.; Keith, A.M.; Kretzschmar, M.; et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 2018, 9, 3033. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Lou, H.; Guo, C.; Fan, B.; Fu, R.; Su, H.; Zhang, J.; Sun, L. Lingonberry (Vaccinium vitis-idaea L.) interact with Lachnum pygmaeum to mitigate drought and promote growth. Front. Plant Sci. 2022, 13, 920338. [Google Scholar] [CrossRef]
  71. Njeru, C.M.; Ekesi, S.; Mohamed, S.A.; Kinyamario, J.I.; Kiboi, S.; Maeda, E.E. Assessing stock and thresholds detection of soil organic carbon and nitrogen along an altitude gradient in an east Africa mountain ecosystem. Geoderma Reg. 2017, 10, 29–38. [Google Scholar] [CrossRef]
  72. Adamczyk, M.; Hagedorn, F.; Wipf, S.; Donhauser, J.; Vittoz, P.; Rixen, C.; Frossard, A.; Theurillat, J.P.; Frey, B. The soil microbiome of GLORIA Mountain summits in the Swiss Alps. Front. Microbiol. 2019, 10, 1080. [Google Scholar] [CrossRef]
  73. Fitzpatrick, C.R.; Copeland, J.; Wang, P.W.; Guttman, D.S.; Kotanen, P.M.; Johnson, M.T.J. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl. Acad. Sci. USA 2018, 115, e1157–e1165. [Google Scholar] [CrossRef] [Green Version]
  74. Hardoim, P.R.; van Overbeek, L.S.; Berg, G.; Pirttilä, A.M.; Compant, S.; Campisano, A.; Döring, M.; Sessitsch, A. The hidden world within plants: Ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol. Biol. Rev. 2015, 79, 293–320. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Forests 14 00685 g001 550
Figure 1. Raincloud plot showing morphological features at different elevations. The half-violin diagram (cloud) shows the kernel density of the data distribution, and the scatter diagram (rain) shows the degree of dispersion. The raincloud plot also includes a box plot (umbrella) and lines (thunder) that link the medians of different groups.
Figure 1. Raincloud plot showing morphological features at different elevations. The half-violin diagram (cloud) shows the kernel density of the data distribution, and the scatter diagram (rain) shows the degree of dispersion. The raincloud plot also includes a box plot (umbrella) and lines (thunder) that link the medians of different groups.
Forests 14 00685 g001
Forests 14 00685 g002 550
Figure 2. Variation in soil and root chemical properties with elevational gradient. The line indicates the linear regression fit, and the shaded band represents the 95% confidence level. R2 was employed to determine the models that fit the whole elevational gradient. OC: organic carbon, TN: total nitrogen, TP: total phosphorus. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
Figure 2. Variation in soil and root chemical properties with elevational gradient. The line indicates the linear regression fit, and the shaded band represents the 95% confidence level. R2 was employed to determine the models that fit the whole elevational gradient. OC: organic carbon, TN: total nitrogen, TP: total phosphorus. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
Forests 14 00685 g002
Forests 14 00685 g003 550
Figure 3. PCoA of the microbial communities of pine roots at different elevations. Significant difference: *** p ≤ 0.001.
Figure 3. PCoA of the microbial communities of pine roots at different elevations. Significant difference: *** p ≤ 0.001.
Forests 14 00685 g003
Forests 14 00685 g004 550
Figure 4. Variation in the relative abundance of genera with elevational gradient. The line indicates the linear regression fit, and the shaded band represents the 95% confidence level. R2 was employed to determine the models that fit the whole elevational gradient. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
Figure 4. Variation in the relative abundance of genera with elevational gradient. The line indicates the linear regression fit, and the shaded band represents the 95% confidence level. R2 was employed to determine the models that fit the whole elevational gradient. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
Forests 14 00685 g004
Forests 14 00685 g005 550
Figure 5. Differentiation of endophytic microbial communities and predicted KEGG or MetaCyc functions predicted by PICRUSt2 between the Pinus yunnanensis-like type and Pinus densata-like type. The PCoA of the root endophytic fungal (a) and bacterial (b) communities of different pine types. The different groups between the P. yunnanensis-like type and P. densata-like type at the phylum (c,d) and genus (e,f) levels. The different functions between rhizosphere soil and roots predicted by FUNGuild (g) and PICRUSt2 (h). Mann–Whitney U tests were used to evaluate differences in the abundance of pine root endophytes. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. “U-” represents “unclassified-”.
Figure 5. Differentiation of endophytic microbial communities and predicted KEGG or MetaCyc functions predicted by PICRUSt2 between the Pinus yunnanensis-like type and Pinus densata-like type. The PCoA of the root endophytic fungal (a) and bacterial (b) communities of different pine types. The different groups between the P. yunnanensis-like type and P. densata-like type at the phylum (c,d) and genus (e,f) levels. The different functions between rhizosphere soil and roots predicted by FUNGuild (g) and PICRUSt2 (h). Mann–Whitney U tests were used to evaluate differences in the abundance of pine root endophytes. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. “U-” represents “unclassified-”.
Forests 14 00685 g005
Forests 14 00685 g006 550
Figure 6. Differentiation of predicted KEGG or MetaCyc functions between the P. yunnanensis-like type and P. densata-like type. The different fungal (a) and bacterial (b) functions between the P. yunnanensis-like type and P. densata-like type predicted by PICRUSt2. Mann–Whitney U tests were used to evaluate differences in the abundance in the abundance of pine root endophytes. Significant difference: * p ≤ 0.05, ** p ≤ 0.01.
Figure 6. Differentiation of predicted KEGG or MetaCyc functions between the P. yunnanensis-like type and P. densata-like type. The different fungal (a) and bacterial (b) functions between the P. yunnanensis-like type and P. densata-like type predicted by PICRUSt2. Mann–Whitney U tests were used to evaluate differences in the abundance in the abundance of pine root endophytes. Significant difference: * p ≤ 0.05, ** p ≤ 0.01.
Forests 14 00685 g006
Forests 14 00685 g007 550
Figure 7. Alpha and beta diversities of microbial communities in rhizosphere soil and roots. Mann–Whitney U tests were used to evaluate differences between rhizosphere soil and roots. The error bar represents one standard error. Significant difference: *** p ≤ 0.001.
Figure 7. Alpha and beta diversities of microbial communities in rhizosphere soil and roots. Mann–Whitney U tests were used to evaluate differences between rhizosphere soil and roots. The error bar represents one standard error. Significant difference: *** p ≤ 0.001.
Forests 14 00685 g007
Forests 14 00685 g008 550
Figure 8. Differentiation of microbial communities in rhizosphere soil and roots. The proportion of root endophytic fungi (a) and bacteria (b) enriched in rhizosphere soil. The different groups between rhizosphere soil and roots at the phylum (c,d) and genus (e,f) levels. Mann–Whitney U tests were used to evaluate differences in the abundance of rhizosphere soil and roots. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. “U-” represents “unclassified-”.
Figure 8. Differentiation of microbial communities in rhizosphere soil and roots. The proportion of root endophytic fungi (a) and bacteria (b) enriched in rhizosphere soil. The different groups between rhizosphere soil and roots at the phylum (c,d) and genus (e,f) levels. Mann–Whitney U tests were used to evaluate differences in the abundance of rhizosphere soil and roots. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. “U-” represents “unclassified-”.
Forests 14 00685 g008
Forests 14 00685 g009 550
Figure 9. Differentiation of predicted KEGG or MetaCyc functions predicted by PICRUSt2 in rhizosphere soil and roots. The different fungal (a) and bacterial (b) functions between rhizosphere soil and roots predicted by PICRUSt2. Mann–Whitney U tests were used to evaluate differences in the abundance of rhizosphere soil and roots. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
Figure 9. Differentiation of predicted KEGG or MetaCyc functions predicted by PICRUSt2 in rhizosphere soil and roots. The different fungal (a) and bacterial (b) functions between rhizosphere soil and roots predicted by PICRUSt2. Mann–Whitney U tests were used to evaluate differences in the abundance of rhizosphere soil and roots. Significant difference: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
Forests 14 00685 g009
Forests 14 00685 g010 550
Figure 10. Dissimilarities of microbial communities in rhizosphere soil and roots based on Bray–Curtis dissimilarity. The fungal community (a) and bacterial community (c) dissimilarities between roots and soil in all samples. The fungal community (b) and bacterial community (d) dissimilarities separately calculated among elevations and at a particular elevation. RA: Root communities among elevations, RW: Root communities at a particular elevation, SA: Soil communities among elevations, SW: Soil communities at a particular elevation. Mann–Whitney U tests were used to evaluate differences in the abundance of rhizosphere soil and roots. Significant difference: *** p ≤ 0.001.
Figure 10. Dissimilarities of microbial communities in rhizosphere soil and roots based on Bray–Curtis dissimilarity. The fungal community (a) and bacterial community (c) dissimilarities between roots and soil in all samples. The fungal community (b) and bacterial community (d) dissimilarities separately calculated among elevations and at a particular elevation. RA: Root communities among elevations, RW: Root communities at a particular elevation, SA: Soil communities among elevations, SW: Soil communities at a particular elevation. Mann–Whitney U tests were used to evaluate differences in the abundance of rhizosphere soil and roots. Significant difference: *** p ≤ 0.001.
Forests 14 00685 g010
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mu, D.; Tang, J.; Cai, N.; Chen, S.; He, Y.; Deng, Z.; Yang, Y.; Yang, D.; Xu, Y.; Chen, L. Effects of Microbial Communities on Elevational Gradient Adaptation Strategies of Pinus yunnanensis Franch. and Pinus densata Mast. in a Mixed Zone. Forests 2023, 14, 685. https://doi.org/10.3390/f14040685

AMA Style

Mu D, Tang J, Cai N, Chen S, He Y, Deng Z, Yang Y, Yang D, Xu Y, Chen L. Effects of Microbial Communities on Elevational Gradient Adaptation Strategies of Pinus yunnanensis Franch. and Pinus densata Mast. in a Mixed Zone. Forests. 2023; 14(4):685. https://doi.org/10.3390/f14040685

Chicago/Turabian Style

Mu, Dejin, Junrong Tang, Nianhui Cai, Shi Chen, Yingnian He, Zijun Deng, Yi Yang, Dan Yang, Yulan Xu, and Lin Chen. 2023. "Effects of Microbial Communities on Elevational Gradient Adaptation Strategies of Pinus yunnanensis Franch. and Pinus densata Mast. in a Mixed Zone" Forests 14, no. 4: 685. https://doi.org/10.3390/f14040685

APA Style

Mu, D., Tang, J., Cai, N., Chen, S., He, Y., Deng, Z., Yang, Y., Yang, D., Xu, Y., & Chen, L. (2023). Effects of Microbial Communities on Elevational Gradient Adaptation Strategies of Pinus yunnanensis Franch. and Pinus densata Mast. in a Mixed Zone. Forests, 14(4), 685. https://doi.org/10.3390/f14040685

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop