Eco-physiological adaptation of dominant tree species at two contrasting karst habitats in southwestern China

Study areas

For the purpose of making comparisons of how the tree species adapt to their own different karst habitats, we designed the experiment at two contrasting mature forest habitats. Tianlongshan, located in the west of Guizhou Province, has a relatively well-developed karst secondary forest. Daxiagu, located in the southwest of Guizhou Province, has a severely degraded karst forest.

Tianlongshan is located about 10 km south of Puding County in western Guizhou Province (26°15′N, 105°44′E) at an altitude of about 1200 m. This region is dominated by a humid monsoon climate. The mean annual temperature is 15.1°C⁴. The mean annual precipitation is 1398 mm, and 60–70% of the rainfall events occur during the growth season between May and October⁴. Tianlongshan has a well-developed secondary evergreen and deciduous broad-leaved mixed forest growing in lime yellow soil. Tree heights range from 3–7 m.

Daxiagu is located 20 km southwest of the town of Huajiang in southwestern Guizhou Province (25°42′N, 105°35′E) at an altitude of about 900 m. This area has a warm temperate climate and a mean annual temperature of 18.4°C⁴. The mean annual precipitation is 1100 mm, with 83% of this precipitation occurring during the growth season between May and October. Vegetation in this region is characterized by sparsely distributed secondary deciduous trees and shrubs on bare rocks.

Field investigations and collections of tree leaf samples in these two sites did not require specific permits. For the purposes of our work, collaboration with local (provincial) universities or research institutes was required. The locations of our field investigations are neither privately-owned nor protected lands. The tree species used in our investigations and sampling were not endangered or protected.

The dominant tree species in Tianlongshan and Daxiagu found in this study are listed in Table 1.

In situ photosynthetic gas exchange and chlorophyll a fluorescence transient measurements

3–4 trees for each species were randomly (the nearest one every 20–30 m distance) selected from the study sites, and a detached branch from each tree was obtained from the top or middle sunny side (south-facing) of the canopy. Branches were detached using a pair of pruning shears mounted on a 5 m pole. The detached branch was immediately immersed in a water-filled bucket. The end of each branch was re-cut twice under water, ensuring continuity of the xylem conduit. Photosynthetic rate (Pn), stomatal conductance (g_s), transpiration rate (Tr), and instantaneous water use efficiency (WUE) were measured with new, fully expanded leaves from the detached branches using a LI-6400 photosynthesis system (LI-COR Inc., Lincoln Nebraska, USA). Measurements were conducted around 8:30–13:00 (Beijing Standard Time). Measurement conditions were set at a PAR (photosynthetically active radiation) level of 1000 µmol m^-2s^-1. Leaf temperature and humidity were at their ambient conditions during the measurement period of June–July 2007 and 2008. During this time, the leaf temperatures were 22–25°C in Tianlongshan and 30–33°C in Daxiagu.

Plant stomatal sensitivity was obtained using Lohammar’s hyperbolic function method^5–7. We measured g_s-VPD (leaf-air vapor pressure deficiency) curves in July 2009, in which the VPD was manually set from saturated vapor [over 85% of relative humidity (RH)] to the driest vapor (about 5% of RH). Leaf temperature was held at 30°C, and PAR at 1000 µmol m^-2s^-1. The g_s-VPD curves were fitted using the modified Lohammar’s function: g_s = -m × ln D + b, to estimate stomatal sensitivity (m)⁸. In this equation, g_s and D stand for stomatal conductance and VPD, respectively, and m and b are parameters generated in a least square regression analysis.

Chlorophyll a fluorescence transients were measured in the leaves from the branches collected as detailed above for photosynthetic gas exchange measurements using a Handy-PEA portable fluorometer (Hansatech Instruments Ltd., Norfolk, UK). Before measurements were taken, the leaves were darkened for at least 30 min using leaf clips (Hansatech Instruments Ltd., Norfolk, UK). The polyphasic chlorophyll afluorescence transients OJIP [fluorescence levels O: F_o (50 μs); J: F_J (2 ms); I: F_I (30 ms), and P: F_p=F_m (tF_max)] were analyzed according to the JIP test procedure^9,10. The JIP test procedure has been widely used in studies of eco-physiology and stress physiology¹⁰. Several parameters can be derived from the following fluorescence values: 50 μs (Fo, step O), 100 μs (F100), 300 μs (F300), 2ms (step J), 30 ms (step I), and the maximum (Fm, step P), using Biolyzer software (version 3.0.7.2 and available by contacting Dr. Reto Strasser at Reto.Strasser@unige.ch). According to Strasser et al.^9,10, the definition of the JIP test parameters used in this study can been summarized as follows: As flux ratios or quantum yields expressed as maximal trapping flux (TRo) to the reaction center of PS II, maximal electron transport flux (ETo) between PS II and PS I, and maximal heat dissipation flux (DIo) of PSII per the light flux absorbed (ABS) by PS II antenna. Φ(Po), maximum quantum yield of primary photochemistry; Φ(Eo), quantum yield of electron transport, probability that an absorbed photon will move an electron into electron transport further than Q_A^-; Φ(Do), quantum yield of dissipation; PI(abs), performance index on the basis of light energy absorption, which is responsible for the performance of the electron transport from water to plastoquinone.

Φ(Po)=1–(Fo/Fm)=Fv/Fm=TRo/ABS; Fo=F50 μs, minimal fluorescence at O-step (50 μs); Fm=FP, maximal fluorescence at P-step;

Φ(Eo)=(Fv/Fm)×Ψ(Eo); Ψ(Eo)=(1–V_J), V_J=(F_J-Fo)/(Fm–Fo), F_J=F2ms, fluorescence at J-step (2ms) of O-J-I-P;

Φ(Do)=1–Φ(Po)=Fo/Fm=DIo/ABS;

PI(abs)=(RC/ABS)×[Φ(Po)/(1–Φ(Po))]×[Ψ(Eo)/(1-Ψ(Eo))]; RC=reaction center, ABS=absorption flux.

Leaf water potential

The midday leaf water potential (Ψ) was determined for each species at the two habitats using a PSYPRO Water Potential System (Wescor, Inc., USA). The measurements were carried out between 11:00 and 14:00 hours (Beijing Standard Time) in August 2009 on leaves from the same detached branches from which the photosynthesis/fluorescence measurements were taken.

Leaf nutrient assay

Leaf total N content and soil total organic N content (%) were determined using the Kjeldahl acid-digestion method (Kjeltec 2200 Auto Distillation Unit, Foss, Denmark). Leaf total phosphorus (P) content and soil total P content (%) were analyzed colorimetrically (UV-visible spectrophotometer, UV-2550, Shimadzu Corporation, Japan).

Statistical analysis

Before the Analysis of Variance (ANOVA) was carried out, all data was examined graphically for the normality of distribution (probability plots for residual analysis), and the homogeneity of variance (scatter plots) using Data Desk (version 6.01, Data Description, Inc., 1996). After examination using the above methods, all data except leaf water potential satisfied the assumption for ANOVA for normal distribution and homogeneity of variance. The data for leaf water potential were log-transformed, and the transformed data satisfied the normal distribution assumption for ANOVA. The effects of habitat (i.e. the severely degraded karst habitat vs. well-developed secondary forest habitat) and life form (i.e. deciduous vs. evergreen) were tested using the two-way ANOVA procedure in Data Desk.

Photosynthetic gas exchange

When measured under their respective ambient conditions in 2007 and 2008 (i.e. ambient temperature and humidity, and PAR set at 1000 µmol m^-2s^-1), the photosynthetic rate (Pn) across all investigated tree species was much higher in Daxiagu (around 2–3 times, P < 0.001) than in Tianlongshan (Figure 1). The g_s and Tr of the investigated tree species in Daxiagu were also much higher (P < 0.001) than those in Tianlongshan (Figure 1). However, when measurements were taken under controlled conditions in 2009 (i.e. PAR was set at 1000 µmol m^-2s^-1, leaf temperature at 30°C, and VPD at 1 KPa), there was no significant habitat effect on Pn (Figure 1). There was a significant difference in Pn between deciduous and evergreen tree species across all investigated tree species in the two habitats. The Pn of deciduous tree species was significantly higher (P < 0.01) than the Pn of evergreen tree species (Figure 1). The g_s and Tr of the deciduous tree species were also significantly higher than the g_s and Tr of evergreen tree species (P < 0.05 and 0.01, respectively). The habitat effect on gs and Tr was not statistically significant (Figure 1). The effects of leaf phenology (deciduous vs. evergreen) and habitat were not significant for instantaneous water use efficiency (WUE) under either ambient or controlled measurement conditions in either habitat.

Figure 1. Photosynthetic gas exchange characteristics.

Effect of habitat (H), leaf phenology (L), and their interaction (H × L), on Pn, g_s, E, and WUE (mean ± SD, n = 3–5) of the tree species in two habitats. T and Dx represent the two study sites Tianlongshan and Daxiagu, respectively. Measurements were conducted under ambient conditions in 2007 and 2008, and under controlled conditions in 2009. The significance levels (*** = P < 0.001, ** = P < 0.01,* = P < 0.05, and ns = P > 0.05) were based on ANOVA results.

Chlorophyll fluorescence

Leaf phenology and habitat had no significant (P > 0.05) effect on chlorophyll fluorescence parameters in 2007 (Figure 2). However, the effect of leaf phenology became significant (P < 0.05) for maximal PSII efficiency (Φ(Po)), quantum yield of PSII electron transport (Φ(Eo)), quantum yield of dissipation (Φ(Do)), and the comprehensive parameter for assessing plant’s vitality: performance index (PI(abs)), for all investigated tree species in 2008 and 2009 (Figure 2). The Φ(Po), Φ(Eo), and PI(abs) of the evergreen tree species were significantly higher than those of the deciduous tree species, while the opposite effect of leaf phenology was seen in Φ(Do).

Figure 2. Photochemical characteristics.

Effects of habitat (H), leaf phenology (L), and their interaction (H × L), on maximal PSII efficiency (Φ(Po)), quantum yield of PSII electron transport (Φ(Eo)), quantum yield of dissipation Φ(Do), and performance index (PI(abs)) (mean ± SD, n = 3–5) of the tree species in two habitats. See Figure 1 for other explanations.

Stomatal sensitivity, leaf water potential, and specific leaf area

Habitat had a significant effect on stomatal sensitivity (m) (P < 0.05), and the stomatal sensitivity (m) was much higher in Daxiagu than in Tianlongshan (Figure 3). There was an interactive effect of habitat and leaf phenology on leaf water potential, and the leaf water potential of deciduous tree species in Daxiagu was significantly lower than the leaf water potential in Tianlongshan (Figure 3). Leaf phenology had a significant effect on the specific leaf area (SLA): the SLA of deciduous species was significantly (P < 0.05) higher than that of evergreen species (Figure 3).

Figure 3. Leaf hydro-physiological and morphological characteristics.

Effects of habitat (H), leaf phenology (L), and their interaction (H × L), on stomatal sensitivity (m), leaf water potential (Ψ) and specific leaf area (SLA) (mean ± SD, n = 3–5) of the tree species in two habitats. The ANOVA for Ψ is based on log-transformed data. See Figure 1 for other explanations.

Leaf nutrients and their efficiencies for photosynthesis

Habitat had significant effects on leaf total N content expressed on the basis of either dry mass or leaf area (P < 0.01) (Figure 4). Leaf total nitrogen content in Daxiagu was much higher than leaf total nitrogen content in Tianlongshan. Leaf phenology also had a significant effect on leaf total nitrogen content expressed on the basis of either dry mass or leaf area (P < 0.01 and P < 0.05, respectively). The mass-based leaf total N content of the deciduous species was much higher than that of the evergreen species. However, when leaf total nitrogen content was expressed on the basis of leaf area, the leaf total N content of the evergreen species was significantly higher than the leaf total N content of the deciduous species. The habitat had a significant effect on leaf total phosphorus content expressed on the basis of either dry mass or leaf area (P < 0.001). The leaf total phosphorus content in Daxiagu was much higher than leaf total phosphorus content in Tianlongshan (Figure 4). However, when leaf total phosphorus content was expressed on basis of leaf area, the values of the evergreen species were much higher than those of deciduous species, especially in Daxiagu. The habitat also had a significant effect on the ratio of leaf total N to P (N:P) for all measured tree species. The N:P ratio in Tianlongshan was significantly (P < 0.001) higher than the N:P ratio in Daxiagu (Figure 4). Leaf phenology had a significant (P < 0.001) effect on photosynthetic nitrogen use efficiency (PNUE), and the PNUE of deciduous species was much higher that of evergreen species (Figure 5). Both habitat and leaf phenology had significant (P < 0.001) effects on photosynthetic phosphorus use efficiency (PPUE). The PPUE of the woody species in Tianlongshan was much higher than that in Daxiagu (P < 0.001), and the PPUE of deciduous species was much higher than that of evergreen species (P < 0.001) (Figure 5).

Figure 4. Leaf nutrient status.

Effects of habitat (H), leaf phenology (L), and their interaction (H × L), on leaf area based- and mass-based total nitrogen (N) and phosphorus (P) (mean ± SD, n = 3–5) of the tree species in two habitats. See Figure 1 for other explanations.

Figure 5. Photosynthetic N and P use efficiency characteristics.

Effects of habitat (H), leaf phenology (L), and their interaction (H × L), on photosynthetic N and P use efficiency (PNUE and PPUE, respectively) (mean ± SD, n = 3–5) of the tree species in two habitats. See Figure 1 for other explanations.