Study location and plant materials
The study was carried out in the wild orchid garden of the Xishuangbanna Tropical Botanical Garden of the Chinese Academy of Sciences (XTBG; latitude: 21.41°, east longitude: 101.25°; elevation: 570 m). XTBG is geographically located on the northern edge of the tropical region, on the gourd-shaped island formed by the Luosuo River, a tributary of the Lancang River, around Menglun town, Xishuangbanna. The area is atmospherically influenced by both Indian Ocean monsoons and East Asian monsoons. The dry and wet seasons are distinct, and the year is divided into a typical rainy season (May–October), a cool foggy season (November-January), and a dry season (February–April). According to XTBG internal ecological station meteorological data, the average annual precipitation at the experimental site is approximately 1560 mm, of which 85% takes place during the rainy season. The cool foggy season is misty at night and in the mornings. There is almost no mist or precipitation during the dry season, and the weather is dry and hot. The average annual temperature of the experimental site is 21.7 °C. The hottest month is July, with an average temperature of 25.5 °C, and the average temperature of the coldest month (January) is 14.8 °C.
In the wild orchid garden, native Litsea liyuyingi, Litsea glutinosa, Lagerstroemia villosa, Mesua ferrea, Melia toosendan, Gardenia sootepensis, and other woody dicotyledons are the main substrates on which Dendrobium and other epiphytic plants grow. To minimize confounding environmental effects, Dendrobium plants selected for this study had been growing in the wild orchid garden for several years and were fully adapted to their growth environment. Each species had at least 10 randomly located, independently growing and healthy plants in the garden. Based on the growth status of the plants and the availability of the plant materials, Dendrobium polyanthum, D. cucullatum, D. loddigesii, D. crepidatum, D. chrysotoxum, D. fimbriatum, D. thyrsiflorum, and D. jenkinsii were selected as the tested species. The eight species are the dominant Dendrobium plants growing in the orchid garden. They are ecologically adapted since they have better growth performances in the orchid garden than other Dendrobium species. The target species belong to two classifications (sect. Chrysotoxae and sect. Dendrobium) with different appearances. In addition, based on the phylogram of 19 Dendrobium species presented in our former paper, the eight species used in the present study are distributed in different clades (Additional file 1: Figure S1). Therefore, the target species here can reflect the genus in terms of general ecological and genetic adaptive strategies to a certain extent.
Determination of traits
All detected traits were listed in additional file 2: Table S1. For each species, six individual plants with good growth conditions were selected. During the flowering period of Dendrobium from March to April, flower number per pseudobulb (FN) was measured directly in situ. Then, three flowers were selected from each plant (18 flowers per species), and the flowers along with their pedicels were taken and placed inside a self-sealing bag containing moistened paper balls. The flowers were quickly placed in a cooler and brought to the nearest laboratory for weighing to obtain flower fresh mass (FFM) and flower petal vein number (FPVN). Then, the flowers were placed in a cowhide envelope and placed in an oven at 70 °C for 48 h until they reached a constant mass to obtain flower dry mass (FDM). Flower water content (FWC) was calculated as (FFM–FDM)/FFM × 100%.
During the growth period of Dendrobium in August, the length of five pseudobulbs (PL) of each plant (30 values per species) were measured in situ with a ruler, and the five-pseudobulb width (PW) and five-pseudobulb internode length (PIL) values of each plant (30 values per species) were measured in situ using a Vernier caliper (precision: 0.01 mm; Guanglu, Guilin, China).
Five healthy leaves were taken from each plant (30 leaves per species). The leaves were separated by plant specimen, placed in a self-sealing bag containing moistened paper balls, and then quickly placed in a cooler and brought to the nearest laboratory to measure the leaf traits. Leaf fresh mass (LFM) was directly determined with an electronic balance (one-thousandth level), and then two of the five leaves were placed in purified water for 48 h to obtain leaf saturated mass (LSM). The leaf areas (LAs) of the other three leaves were measured by a Li-Cor 3000A area meter (Li-Cor Inc., Lincoln, NE, USA); then, the leaves were placed in a cowhide envelope and dried in a 70 °C oven for 48 h until they reached a constant weight, which was leaf dry mass (LDM). Finally, the two leaves measured for LSM were also dried, and LDM was measured. Leaf water content (LWC) was calculated as (LFM–LDM)/LFM × 100%; leaf dry matter content (LDMC) was calculated as LDM/LSM × 100%; and and specific leaf area (SLA) was calculated as LDM/LA (leaf area per dry mass).
To characterize leaf anatomy, transverse sections at the midpoint of the leaves were hand-cut, stained for 1 min with 0.1% toluidine blue, rinsed with distilled water, and photographed under a DM2500 light microscope (Leica Inc., Bensheim, Germany). Mesophyll thickness (MT), upper epidermal cell size (UECS) and stomatal pore depth (SPD) were then measured from the digital photographs with ImageJ v.1.48 software (http://rsbweb.nih.gov/ij/).
The abaxial midpoints of the leaves were pasted onto pellucid enamels and then transferred to glass slides after drying. The stomatal prints on the enamels were photographed under a DM2500 light microscope, and stomatal traits were measured with ImageJ software. Stomatal density (SD) was measured as the number of stomata per unit area and was calculated as the mean value of 30 digital images for each species (5 images per plant). Stomatal length (SL) and stomatal width (SW) were averaged from 30 randomly selected stomata for each species. The stomatal area index (SAI) was estimated by the formula SD × SL2 (Sack et al. 2003). The stomatal volume (SV) was estimated by SL × SW × SPD. After measuring the stomata, we completely removed the enamel and slowly scraped off the mesophyll with a double blade, mounted it on slides, and photographed it. Total vein length was measured manually with ImageJ software, and vein density (VD) was calculated as total vein length per area.
Building a phylogenetic tree
The phylogenetic tree in this study was constructed based on splicing nuclear ITS and rbcL sequences and chloroplast matK and ycf5 sequences. These gene sequences were downloaded from GenBank (http://www.ncbi.nlm.nih.gov). Because the genus Dendrobium is closely related to Bulbophyllum (Freudenstein and Rasmussen 1999), this study selected Bulbophyllum odoratissimum as the outgroup. Sequence alignment was performed using the “CLUSTALW” module of MEGA v.5.0 software. Model selection was performed using ModelTest v.3.7 software, and the optimal model was selected using the Akaike information criterion. The GTR (general time reversible) + G (gamma shape) model was the optimal model for the dataset in this study. Using MrBayes v.3.2 software, phylogenetic analysis of the gene sequence matrix was carried out by the Bayesian method, and a phylogenetic tree was constructed. The analysis was run 100,000 times, and a relatively stable phylogenetic tree (Fig. 1) was selected for the study. A posttest was used to estimate the stability of the nodes.
Statistical analysis
Prior to analysis, all plant trait data were log10 transformed to increase normality and variance homogeneity. The statistical analyses were mainly performed using R v.3.01 (http://ftp.ctex.org/mirrors/CRAN/). The “vegan” package in R software was used to perform principal component analysis (PCA) of the species mean values of all traits and explore the main relationships between plant trait changes on the first two major axes.
To assess the phylogenetic conservation of traits, we first applied the “picante” package in R software (Kembel et al. 2010) to detect the phylogenetic signal of these traits (K value) based on the K-statistics, which is based on the assumption of Brownian motion trait evolution. K > 1 indicates that the trait is more conserved and is strongly influenced by phylogeny; K < 1 indicates that the variability of the trait is higher and is less affected by phylogeny; and K = 1 indicates that the trait follows the random variation found in the Brownian motion model (Blomberg et al. 2003).
Phylogenetic independent contrast (PIC) analysis first uses the analysis of traits (AOT) module of Phylocom software to calculate the node contrast values of leaf traits (Webb et al. 2008). These contrasts were calculated as trait differences between two sister species pairs at the tips and were subsequently weighted to obtain an internal node average. Then, they were divided by the expected amount of change, which was calculated as the square root of the branch length separating the two taxa. These comparisons provide N − 1 (N refers to the number of species, N = 8 in this study) independent data points, with each point representing evolutionary divergence (Ackerly 1999; Ackerly and Reich 1999). Then, using Pearson correlation analysis in R software, we calculated the correlations between traits before and after PIC analysis.