b. Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;
c. Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences, Hechi 547100, China;
d. Guangdong Provincial Key Laboratory of Applied Botany, State Key Laboratory of Plant Diversity and Specialty Crops, Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
Temperature and water are critical environmental factors that directly influence plant growth and distribution (Nievola et al., 2017; Wen et al., 2018; Jiao et al., 2021). In the context of global climate change, the rising frequency of extreme temperature and drought events poses significant threats to plant survival and has been contributing to widespread forest decline and tree mortality (Allen et al., 2010; Arnold et al., 2021; Ayala-Jacobo et al., 2021; Marchin et al., 2022). The latest IPCC report indicates that, if current greenhouse gas emission trends persist, the global average surface temperature is projected to increase by 1.5–2 ℃ by the end of this century (Kikstra et al., 2022). This rise is expected to result in a higher frequency and intensity of extreme temperature and drought events, which will, in turn, exert severe impacts on ecosystems (Zandalinas et al., 2021; Wen et al., 2023; Sato et al., 2024). Therefore, elucidating the mechanisms of plant adaptation to coupled temperature and water stress is vital for predicting their responses to climate change, modeling distributional shifts, and developing effective ecological conservation strategies.
Plants have evolved diverse physiological tolerances, including heat, cold, and drought tolerance, to cope with multiple and often simultaneous environmental stresses (Niinemets and Valladares, 2006; Grossman, 2023). However, current research predominantly focuses on the response mechanisms of single tolerance traits, with only approximately 5% of studies simultaneously addressing heat and cold tolerance, and about 6% examining relationships between heat tolerance and water availability (Geange et al., 2021). Furthermore, most of these studies have been conducted in temperate or tropical ecosystems, while extreme habitats characterized by excessive cold, drought, heat, or unique geological features—such as karst ecosystems—remain largely understudied. As a result, our understanding of plant tolerance under multiple, interacting environmental stresses remains limited. Previous studies have shown that different tolerances can interact and evolve synergistically, e.g., plants in cold regions tend to exhibit enhanced cold and drought tolerance, while species in arid environments are more heat tolerant (Bansal et al., 2016; Münchinger et al., 2023). Nonetheless, trade-offs between tolerances may also occur, limiting plant performance under multiple simultaneous stresses and constraining the ability of plants to optimize tolerance across different environmental challenges (Stahl et al., 2013; Puglielli et al., 2023). Therefore, the relationships among different physiological tolerances remain poorly understood.
Leaf morphological and anatomical traits play a pivotal role in plant adaptation to drought and temperature stress by regulating temperature and water exchange, thereby forming diverse ecological strategies (Stahl et al., 2014; Valliere et al., 2023). First, leaves can adjust the thickness of the boundary layer, thereby affecting the rate of heating or cooling, where larger leaf width (LW) and leaf area (LA) increase boundary layer thickness, reduce heat exchange with the air, and increase the risk of thermal damage (Jones, 2014; Slot et al., 2021). Second, leaves may reduce water loss and conserve water by increasing leaf thickness (LT) and decreasing LA, aiding survival in arid environments (Afzal et al., 2017; Maréchaux et al., 2020). Additionally, leaf mass per area (LMA) serves as a central trait of plant physiological tolerance to drought and temperature stress. Leaves with low LMA are typically thinner, losing water more rapidly in drought but facilitating rapid growth and photosynthesis in moist environments (De La Riva et al., 2016). While high LMA leaves are more compact with thicker cell walls, providing greater tolerance to extreme high temperatures and drought (De La Riva et al., 2016; Zhang et al., 2024b). These findings suggest that key leaf morphological and anatomical traits are crucial for plant adaptation to varying stress conditions, and more research is needed to reveal the underlying mechanisms.
The karst region in southwestern China is the largest and most typical contiguous karst area in the world (Cai, 1997; Lv et al., 2023), with the northern tropical karst forests of Guangxi being an important component. Plants in this region face unique environmental pressures, including exposed bedrock, shallow soils, and high concentrations of calcium (Ca) and magnesium (Mg), as well as frequent human disturbances (Wang et al., 2018; Tang et al., 2021). Critically, the extensive fissures and pores in karst landscapes result in severe water shortages (Yao et al., 2001; Wilcox et al., 2008; Guan et al., 2023), making vegetation highly vulnerable to temperature and water stress (Wang et al., 2023a). Consequently, global climate change is expected to further intensify these stresses, posing even greater survival challenges for the already vulnerable and sensitive karst ecosystems (Seddon et al., 2016; Zhang et al., 2022). To cope with such harsh environments, plants in karst regions are thought to have developed specific physiological tolerances. In addition, phylogeny plays an important role in shaping and constraining the evolution of physiological tolerance through phylogenetic conservatism. However, the extent to which lithology (karst environment) and phylogeny explain variation in the physiological tolerance of karst species remains unclear.
Comparing plant species from karst and adjacent non-karst forests under similar climatic conditions provides a robust approach to identifying the unique adaptations of karst species. In southwestern China, the close proximity of these contrasting habitats under comparable climates creates a natural laboratory to directly investigate how lithology shapes plant physiological adaptations. In this study, we focused on dominant woody species from two karst and non-karst forests in the northern tropical region of Guangxi. We measured leaf physiological tolerance to drought and temperature stress, along with key morphological and anatomical traits, to explore the mechanisms of plant adaptation under similar climatic but contrasting lithological conditions, thereby providing a scientific basis for the conservation and restoration of karst forests. Specifically, the study addresses two scientific questions: (1) How do leaf physiological tolerances to drought and temperature differ between karst and non-karst species, and how are these differences shaped by lithology and phylogeny? (2) How are leaf physiological tolerances associated to morphological and anatomical traits? We hypothesize that, due to the unique and harsh environmental pressures in karst habitats, karst species will exhibit greater leaf physiological tolerance to drought and heat than non-karst species. We further expect that these tolerances to be closely associated with key leaf morphological and anatomical traits, such as LT and LMA.
2. Materials and methods 2.1. Study sitesExperiments were conducted at two nature reserves in Guangxi, China: Nonggang (22.47°N, 106.95°E; elevation 273 m, karst) and Shiwandashan (21.84°N, 107.89°E; elevation 558 m, non-karst). These forest sites are geographically close and share similar climatic conditions, with Nonggang having a mean annual temperature of 21.8 ℃ and annual precipitation of 1666.2 mm, and Shiwandashan having a mean annual temperature of 19.3 ℃ and annual precipitation of 1946.6 mm (Table S1). However, the two reserves differ markedly in lithology. Nonggang is typical karst topography, characterized by shallow soils rich in Ca and Mg (Li et al., 2023), and their vegetation is tropical rainforest. In contrast, the Shiwandashan features non-karst topography, primarily consisting of sedimentary, metamorphic, and igneous rocks, and with a northern tropical semi-evergreen seasonal rainforest and moist rainforest.
2.2. Species selection and samplingBased on community survey data, we selected the 19 and 20 most abundant evergreen woody species from karst and non-karst forests, respectively. Heat tolerance experiments and turgor loss points (πtlp) measurements were conducted from July to August 2023, while cold tolerance experiments were performed from January to February 2024, aligning with the corresponding seasonal stresses periods. Turgor loss point was measured in the same season as heat tolerance assessments because these two stressors typically co-occur.
For each species, we sampled three to five healthy, mature individuals. Leaf collection was conducted between 07:30 and 09:30 on sunny days to minimize the effects of diurnal variation in water content and photosystem activity. Approximately 60 fully expanded, sun-exposed, and mature leaves were collected per species, immediately placed in black plastic bags with damp paper towels, sprayed with water to maintain high humidity, and promptly transported to the laboratory for subsequent measurements. Due to specific experimental requirements and practical constraints, the number of leaves per treatment varied slightly across experiments (typically five to ten leaves). To ensure representative sampling, leaves for each treatment were consistently collected from three to five healthy individuals.
2.3. Photosynthetic heat toleranceFollowing Krause et al. (2010), we assessed photosynthetic heat tolerance by measuring the temperature at which the maximum quantum yield of PSII (Fv/Fm) begins to decrease (Tcritheat) and the temperature that causes a 50% decrease in Fv/Fm (T50heat) under high-temperature conditions (Kalaji et al., 2014; O'sullivan et al., 2017). Leaves were cut into 4 cm2 squares (or used whole if smaller) and dark-adapted for 1 h. Fv/Fm was then measured at room temperature (approximately 28 ℃) using a LI-600 portable fluorometer (LI-COR, Lincoln, USA); only leaves with an initial Fv/Fm > 0.7 were used to exclude photoinhibition samples. Each leaf piece was subjected to only one temperature treatment to avoid cumulative heat damage. Leaf pieces were placed into separate sealed plastic bags (two non-overlapping pieces per bag) to ensure uniform heating, and exposed to water bath treatments at one of the following temperatures: 36, 40, 44, 46, 48, 50, 52, 54, 56, or 60 ℃, for 15 min (Eight replicates per species at each temperature). After treatment, the samples were dark-adapted for 24 h before measuring the Fv/Fm. Tcritheat and T50heat were calculated by fitting the Weibull model using the method and R code developed by Perez et al. (2021).
2.4. Photosynthetic cold toleranceFollowing Sierra-Almeida et al. (2009), we assessed photosynthetic cold tolerance by measuring the temperature at which the maximum quantum yield of PSII (Fv/Fm) begins to decrease (Tcritcold) and the temperature that causes a 50% decrease in Fv/Fm (T50cold) under low-temperature conditions (Knaupp et al., 2011; Pérez et al., 2014). Leaves were dark-adapted for 1 h and their Fv/Fm values were measured at room temperature conditions (approximately 18 ℃) using a LI-600 portable fluorometer. Each leaf sample was subjected to only one cold-temperature treatment to prevent cumulative cold injury. Fully expanded, mature leaves were then attached to Type K thermocouples (supplied with the thermal data logger, Yongpeng, YP5064, China) and fixed in a 10 mm thick foam box. The box was then placed into a variable-frequency refrigerator (Aucma, BD-186WPHNXI, China) pre-cooled to one of the following temperatures: 4, 0, −2, −4, −6, −8, −12, −16, or −24 ℃ (Five replicates per species at each temperature). The foam box buffered heat exchange, allowing leaves to cool gradually and minimizing the effects of rapid temperature decline (Arnold et al., 2021). To account for internal temperature fluctuations, real-time leaf temperatures were monitored via thermocouples, and used for subsequent data fitting. Each temperature treatment lasted for 2 h, after which the foam box containing the leaves was removed and placed at 4 ℃ to allow gradual warming and minimize rapid freeze-thaw injury. The leaves were then dark-adapted at 4 ℃ for 24 h before final Fv/Fm measurement. Tcritcold and T50cold were calculated using a Weibull model fitted with a modified version of the R code from Perez et al. (2021).
2.5. Turgor loss pointDrought tolerance was assessed using πtlp, following the protocol of Bartlett et al. (2012), a parameter validated as biologically meaningful in drought response analyses (Bartlett et al., 2014, 2016). Sampling was conducted following the same procedure as for heat tolerance experiments. Mature, well-hydrated leaves were collected in the early morning and rehydrated in the laboratory for approximately 20 min to ensure full turgor. For each leaf, small discs were punched from the central lamina (ten replicates per species), wrapped in aluminum foil, labeled, and immediately flash-frozen in liquid nitrogen. After 2 min, the samples were removed, punctured approximately 20 times with a fine needle, and promptly placed in the sample chamber of a osmometer (Wescor, Vapor 5600, USA) to measure the osmotic potential at full turgor (πosm). The πtlp was calculated using the following formula: πtlp = 0.832πosm − 0.631 (Bartlett et al., 2012).
2.6. Leaf morphological and anatomical traitsFor each species, we selected ten healthy and mature leaves for the measurement of morphological and anatomical traits. After carefully removing surface impurities, the leaves were scanned using a flatbed scanner (Epson, V850 Pro, Japan). Leaf width (LW, cm), leaf length (LL, cm), leaf perimeter (LP, cm), and leaf area (LA, cm2) were measured from the scanned images using ImageJ software (v.1.52n). The scanned leaves were then placed into pre-labeled envelopes and dried in an oven (Thermo Fisher Scientific, OMH400, USA) at 75 ℃ for 48 h before being weighed to obtain the leaf dry weight (LDW, g). Meanwhile, additional healthy and mature leaves were selected, from which the main vein was removed. Cross-sections were prepared using a double-edged stainless-steel blade to create temporary slides for observation under a light microscope. Leaf anatomical parameters—including leaf thickness (LT, μm), upper epidermis thickness (Tup, μm), lower epidermis thickness (Tlow, μm), palisade mesophyll thickness (Tp, μm), and spongy mesophyll thickness (Ts, μm)—were measured in micrometers using ImageJ software on temporary slides. Finally, we calculated the leaf length-to-width ratio (L/W), the palisade-to-spongy mesophyll thickness ratio (P/S), and the leaf mass per area (LMA, g/m2). All the traits measured are listed in Table 1.
| Abbreviation | Description | Unit |
| Tcritheat | The high-temperature at which the maximum photochemical quantum yield of photosystem Ⅱ (Fv/Fm) begins to decrease | ℃ |
| T50heat | The high-temperature that causes a 50% decrease in the maximum photochemical quantum yield of photosystem Ⅱ (Fv/Fm) | ℃ |
| Tcritcold | The low-temperature at which the maximum photochemical quantum yield of photosystem Ⅱ (Fv/Fm) begins to decrease | ℃ |
| T50cold | The low-temperature that causes a 50% decrease in the maximum photochemical quantum yield of photosystem Ⅱ (Fv/Fm) | ℃ |
| πtlp | Leaf water potential at turgor loss | MPa |
| Tup | Upper epidermis thickness | μm |
| Tlow | Lower epidermis thickness | μm |
| Tp | Palisade mesophyll thickness | μm |
| Ts | Spongy mesophyll thickness | μm |
| P/S | The palisade-to-spongy mesophyll thickness ratio | Dimensionless |
| LT | Leaf thickness | μm |
| LW | Leaf width | cm |
| LL | Leaf length | cm |
| L/W | Leaf length-to-width ratio | Dimensionless |
| LP | Leaf perimeter | cm |
| LA | Leaf area | cm2 |
| LMA | Leaf mass per area | g/m2 |
All data analyses and visualizations were conducted in R (v.4.3.3; R Core Team, 2023). To explore the covariation of plant traits across the two habitats, principal component analysis (PCA) was applied to all measured traits, and the relationships among leaf physiological tolerance traits and morphological and anatomical traits were evaluated using Pearson correlation analysis. Differences in all measured traits between the two habitats were assessed using Welch's two-sample t-tests.
To examine phylogenetic signals and quantify the effects of phylogeny on trait variation, we constructed a phylogenetic tree using the V.PhyloMaker package (v.0.1.0). Phylogenetic signals for each trait were assessed by calculating Blomberg's K values (Blomberg et al., 2003) using the picante package (v.1.8.2). To further partition the variance in physiological tolerance traits and principal components (PC1 and PC2), and to quantify the extent to which lithology and phylogeny explain trait variation, we applied phylogenetic eigenvector regression (PVR) (Desdevises et al., 2003; Liu et al., 2024). PVR fits a single linear regression model to analyze a dependent variable (trait y) with two predictors: lithology (karst or non-karst environments) and phylogenetic eigenvectors derived from a phylogenetic distance matrix. The variance in y was partitioned into four components: [a] + [b] + [c] + [d], where [a] is the variation explained by lithology, [b] is the shared variation explained by both lithology and phylogeny, [c] is the variation explained by phylogeny, and [d] is the unexplained variance.
3. Results 3.1. Differences in leaf traits between karst and non-karst plantsKarst species exhibited significantly lower photosynthetic heat tolerance (T50heat: 49.97 ℃ vs. 50.92 ℃; Tcritheat: 40.48 ℃ vs. 45.74 ℃) and more negative turgor loss point (πtlp: −1.98 MPa vs. −1.40 MPa) compared to non-karst species. In contrast, no significant differences were observed in T50cold and Tcritcold between the two habitats (Table 2).
| Traits | Karst (n = 19) | Non- karst (n = 20) | t | p | ||
| T50heat (℃) | 49.97 ± 0.38 | 50.92 ± 0.26 | −2.06 | < 0.05 * | ||
| Tcritheat (℃) | 40.48 ± 0.71 | 45.74 ± 0.59 | −5.68 | < 0.01** | ||
| T50cold (℃) | −8.72 ± 0.60 | −7.19 ± 0.50 | −1.96 | 0.06 | ||
| Tcritcold (℃) | 0.18 ± 0.79 | −1.83 ± 0.62 | 2.01 | 0.0523 | ||
| πtlp (MPa) | −1.98 ± 0.11 | −1.40 ± 0.05 | −4.73 | < 0.01** | ||
| Tup (μm) | 21.37 ± 1.27 | 32.58 ± 2.72 | −3.73 | < 0.01** | ||
| Tlow (μm) | 16.66 ± 0.81 | 24.58 ± 2.09 | −3.53 | < 0.01** | ||
| Tp (μm) | 42.20 ± 3.48 | 55.00 ± 5.09 | −2.07 | < 0.05 * | ||
| Ts (μm) | 81.59 ± 11.28 | 131.41 ± 15.07 | −2.65 | < 0.05 * | ||
| P/S | 0.69 ± 0.11 | 0.48 ± 0.04 | 1.79 | 0.09 | ||
| LT (μm) | 165.05 ± 12.59 | 241.51 ± 17.63 | −3.53 | < 0.01** | ||
| LW (cm) | 5.54 ± 0.76 | 3.80 ± 0.21 | 2.21 | < 0.05 * | ||
| LL (cm) | 11.92 ± 1.03 | 10.73 ± 0.54 | 1.03 | 0.31 | ||
| L/W | 2.38 ± 0.14 | 2.90 ± 0.12 | −2.84 | < 0.01** | ||
| LP (cm) | 31.09 ± 3.53 | 24.77 ± 1.21 | 1.69 | 0.10 | ||
| LA (cm2) | 51.20 ± 11.77 | 27.14 ± 2.59 | 2.00 | 0.06 | ||
| LMA (g/m2) | 66.57 ± 5.78 | 82.64 ± 6.34 | −1.87 | 0.07 | ||
| Data are mean ± SE, sample sizes (n) are given in brackets. * and ** indicates p < 0.05 and p < 0.01, respectively. Abbreviations are in Table 1 | ||||||
For leaf morphological and anatomical traits, karst species had significantly lower values of LT, Tup, Tlow, Tp, Ts, and L/W, but greater LW. No significant differences were found in other traits between habitats (Table 2).
Principal component analysis (PCA) revealed clear separation between karst and non-karst species along the first two principal components. PC1 accounted for 31.9% of the total variance and was primarily influenced by morphological traits such as LW, LA, LT, and LP. PC2 explained 19.2% of the variance and was predominantly driven by photosynthetic heat tolerances (T50heat and Tcritheat) and Tp, LW, and LA. Together, PC1 and PC2 contributed 51.1% of the total variance (Fig. 1). Overall, the distinctions between karst and non-karst forests were mainly driven by heat tolerance and leaf morphological traits.
|
| Fig. 1 Principal component analysis of Karst and Non-karst evergreen woody species based on 17 leaf functional traits. (a) Loading of first two principal components (PC), and (b) scores for each species along PC1 and PC2, with Karst species (blue circles) and Non-karst species (orange triangles). Vectors in the (a) represent individual leaf functional traits, colored by categories: red for Physiological Tolerance, green for Morphological Traits, and yellow for Anatomy Traits. (c and d) Contribution of variables to the first and second PC, respectively ranked from high to low. Red dashed lines in (c) and (d) indicate average contribution of each variable. Abbreviations are in Table 1. |
Among physiological tolerances, πtlp (K = 0.69, p = 0.007) and T50cold (K = 0.52, p = 0.043) showed significant phylogenetic signals (Table 3). PVR analysis revealed that lithology explained a higher proportion of variance for πtlp (both single and joint effects R2 = 0.386, hereafter as joint R2) and PC1 (joint R2 = 0.409), while phylogeny significantly explained the variance for T50cold (joint R2 = 0.156) and PC2 (joint R2 = 0.140). Despite this, a substantial proportion of variation in all traits remained unexplained, ranging from 48.0% to 76.3%, with particularly high residuals for T50heat, Tcritcold, and PC2 (Fig. 2; Table S2).
| Traits | Blomberg's K | p |
| T50heat (℃) | 0.24 | 0.61 |
| Tcritheat (℃) | 0.43 | 0.12 |
| T50cold (℃) | 0.52 | 0.043 |
| Tcritcold (℃) | 0.45 | 0.09 |
| πtlp (MPa) | 0.69 | 0.007 |
| Tup (μm) | 0.35 | 0.34 |
| Tlow (μm) | 0.19 | 0.82 |
| Tp (μm) | 0.23 | 0.66 |
| Ts (μm) | 0.53 | 0.06 |
| P/S | 0.38 | 0.28 |
| LT (μm) | 0.42 | 0.15 |
| LW (cm) | 0.45 | 0.26 |
| LL (cm) | 0.28 | 0.50 |
| L/W | 0.27 | 0.47 |
| LP (cm) | 0.32 | 0.46 |
| LA (cm2) | 0.40 | 0.38 |
| LMA (g/m2) | 0.27 | 0.53 |
| Bold indicates significant phylogenetic signals (p < 0.05). The phylogenetic tree used for this analysis is presented in Figure S2. | ||
|
| Fig. 2 Relative contributions to total variance in plant functional traits of studied Karst and Non-karst species. Models are based on lithology variables, phylogeny factors. The bars represent the variance explained by pure lithology (red), pure phylogeny (blue), interaction effects from two factors (light blue), and unexplained variance (grey). Each row corresponds to a specific plant functional trait, and the segments within each bar indicate the proportion of variance attributed to each factor or interaction. Abbreviations are in Table 1, detailed parameters in Table S2. |
T50cold was significantly negatively correlated with T50heat (R2 = 0.19, p = 0.01) and significantly positively correlated with πtlp (R2 = 0.18, p = 0.01), while T50heat was not correlated with πtlp (R2 < 0.01, p = 0.79) (Fig. 3). Among leaf morphological and anatomical traits, LT and LMA were both positively correlated with T50heat (R2 = 0.16, p = 0.01; and R2 = 0.14, p = 0.02, respectively), while LT was also positively correlated with Tcritheat (R2 = 0.16, p = 0.01) (Fig. 4). Additionally, Tp was positively correlated with both T50heat and Tcritheat, while Tup and Tlow were both positively correlated with πtlp (Fig. S1). No other significant correlations were observed between other morphological and anatomical traits and physiological tolerances.
|
| Fig. 3 Correlations of (a) the high-temperature that causes 50% decrease of the maximum photochemical quantum yield of photosystem Ⅱ (T50heat) and the low-temperature that causes 50% decrease of the maximum photochemical quantum yield of photosystem Ⅱ (T50cold), (b) T50cold and the leaf water potential at the turgor loss (πtlp), (c) T50heat and πtlp for the 39 evergreen woody species of Karst (blue) and Non-karst (orange). Significant correlations are shown as solid lines, and non-significant ones are shown as dashed lines. |
|
| Fig. 4 Correlations of the high-temperature at which the maximum photochemical quantum yield of photosystem Ⅱ (Fv/Fm) begins to decrease (Tcritheat) and high-temperature that causes 50% decrease of the maximum photochemical quantum yield of photosystem Ⅱ (T50heat) with leaf thickness (LT) and Leaf mass per area (LMA) in 39 evergreen woody species of Karst (blue) and Non-karst (orange). Significant correlations are shown as solid lines, and non-significant ones are shown as dashed lines. |
In line with our hypothesis, karst species exhibit more negative πtlp, consistent with previous findings that karst plants tend to maintain lower leaf water potential and πtlp than non-karst species (Chen et al., 2015; Wan et al., 2023). This suggests that πtlp is likely shaped by the unique selection pressures of karst habitats, where complex geological structures and widespread exposed bedrock create heterogeneous microhabitats with shallow soils and poor water retention (Fu et al., 2019; Qi et al., 2021). These conditions impose persistent drought stress, driving the evolution of specialized drought tolerance strategies. However, contrary to our expectations, karst species showed lower photosynthetic heat tolerance than non-karst species. This may be because, under long-term drought conditions, leaf photosynthesis is more constrained by water availability than by temperature (Chaves et al., 2002; Pinheiro and Chaves, 2011). Besides, the exposed rocky environment and intense solar radiation in karst habitats likely result in higher leaf temperatures in karst species compared to non-karst species. Consequently, both the elevated leaf temperatures and lower heat tolerance of karst plants act to narrow their thermal safety margin, making them more susceptible to thermal stress. This vulnerability is further exacerbated by their thinner leaves, which provide reduced thermal buffering capacity and lower resistance to extreme heat (Leigh et al., 2012; Marchin et al., 2022). Consequently, despite their enhanced drought tolerance, karst species may be more vulnerable to thermal damage under global warming scenarios. Cold tolerance did not differ significantly between the two habitats, likely reflecting long-term adaptation to similar regional climatic conditions, which impose comparable cold selective pressures. Cold tolerance is strongly influenced by evolutionary history and niche conservatism (Hawkins et al., 2014) and can be enhanced through cold acclimation (Cavender-Bares et al., 2005; Ayala-Jacobo et al., 2021). Thus, similar climatic conditions among our study sites appear to constrain divergence in cold tolerance and promote convergent cold tolerance strategies in both karst and non-karst species.
4.2. Lithology and phylogeny in explaining trait variationThe results for T50cold and πtlp show significant phylogenetic signals, supporting evolutionary conservatism in plant adaptations to cold and drought, consistent with results of Lancaster and Humphreys (2020) and Liu et al. (2024). Although πtlp exhibits evolutionary conservatism, PVR analysis reveals that lithology—the unique geological conditions of karst habitats—accounts for 38.6% of its variance, indicating that strong environmental selection may drive the adaptive evolution of this trait. Similarly, karst lithology drives the evolution of traits such as deep root systems and high leaf hydraulic conductance, which enhance drought tolerance by accessing water in karst and maintaining efficient water transport fissures in dry soils (Du et al., 2023; Wan et al., 2023; Ma et al., 2024). In contrast, neither T50heat nor the most leaf morphological and anatomical traits display significant phylogenetic signals. Instead, PVR analysis further indicates that T50heat and PC1 (which primarily represents leaf morphology) are more strongly influenced by lithological selection rather than phylogenetic constraints. Several studies suggest that heat tolerance is mainly driven by environmental factors and is closely associated with leaf morphological and anatomical traits (Perez and Feeley, 2021; Ye et al., 2023). For example, plants can regulate leaf temperature by modifying leaf morphological and anatomical traits, such as increasing LMA and LT, or decreasing LA, which mitigates the damage caused by extreme temperatures and thereby influences heat tolerance (Slot et al., 2021; Zhou and Lin, 2023). Additionally, karst species often accumulate higher Ca and Mg in their leaves and display enhanced mechanical resistance (Fu et al., 2019; Wan et al., 2023, 2025), further supporting the role of lithological filtering in shaping PC1. Despite these clear patterns, a substantial portion of trait variation remained unexplained, which may be attributable to unaccounted environmental variables such as soil moisture and light intensity, trait plasticity.
4.3. Relationships among plant leaf physiological toleranceOur findings show a positive correlation between heat and cold tolerance, indicating that species with greater heat tolerance also tend to withstand low-temperature stress. This contrasts with observations that plants from warmer regions often exhibit greater heat tolerance (Feeley et al., 2020; Slot et al., 2021). This relationship between thermal tolerance likely arises from shared physiological stresses, such as cellular structural damage, membrane dysfunction, and oxidative stress, imposed by both high and low temperature extremes, which drive plants to develop convergent adaptive strategies (Gill et al., 2010; Liu et al., 2017; Cheema and Garg, 2021). However, to our knowledge, no studies report a negative heat-cold tolerance relationship in woody plants, highlighting the need for further validation. We also found a positive correlation between cold and drought tolerance, potentially driven by seasonal water stress. Winter cold temperatures are often accompanied by reduced precipitation and soil moisture deficits, while summer droughts lead to an imbalance between transpiration demand and water supply, both of which challenge plants with insufficient water availability (Bray, 1997; Sato et al., 2024; Zhang et al., 2024a). This seasonal water stress drives plants to develop convergent adaptive strategies, such as reducing vessel diameter and enhancing xylem resistance to embolism (Lobo et al., 2018; Levionnois et al., 2021), thereby improving their survival in cold, arid environments. In contrast to Münchinger et al. (2023), we found no correlation between heat and drought tolerance. This decoupling may result from the differences in the timescales of plant responses to these stresses: heat waves typically last for short periods (hours to days), requiring rapid plant responses, such as inducing heat shock protein expression within hours to maintain photosynthetic and cellular structural stability (Havaux, 1993; Grossman, 2023), whereas drought persists over longer periods (weeks to months), compelling plants to gradually adapt to water deficits through mechanisms such as osmotic adjustment, increased water use efficiency, and root system modifications (Havaux, 1993; Grossman, 2023). These differences in rapid versus long-term responses may result in independent adaptive mechanisms for heat and drought tolerance, rendering the correlation between them insignificant. Overall, the interplay of synergistic co-tolerance and trade-offs among multiple physiological tolerances enables plants not only to adapt to a wide range of temperatures from heat to cold but also to cope with seasonal water stress, thereby expanding their ecological niches.
4.4. Associations between leaf physiological tolerance traits and morphological and anatomical traitsBoth LT and LMA were positively correlated with heat tolerance (LT with T50heat and Tcritheat, LMA with T50heat), supporting the idea that greater structural investment enhances resistance to extreme heat (Zhang et al., 2024b). Thicker leaves may contain more cell layers and protective tissues or possess greater water storage capacity content to offset the effects of warming (Kröber et al., 2015; Leigh et al., 2017; Tserej and Feeley, 2021). Similarly, higher LMA typically indicates tougher tissues and longer leaf lifespan (Wright et al., 2004; Wang et al., 2023b), providing better mechanical support and structural stability, which enhances adaptability and enables plants to maintain normal photosynthesis under high-temperature stress (Sastry and Barua, 2017; Wan et al., 2023). These traits collectively enhance physiological tolerance, helping to mitigate damage caused by heat stress (Sastry et al., 2018; Zhang et al., 2024b). However, this study did not find a correlation between LA and heat tolerance, which is consistent with the conclusions of most scholars (Sastry and Barua, 2017; Wright et al., 2017; Ning et al., 2024), suggesting that the correlation between LA and heat tolerance is not universal and may be specific to certain high-temperature, arid environments (Leigh et al., 2017; Sastry and Barua, 2017).
Contrary to our initial expectations, LT, LA, and LMA showed no significant correlation with cold or drought tolerance. This may be because cold tolerance is often depends on physiological and biochemical adjustments (such as membrane stability, antifreeze proteins, and osmoprotectants) (Liu et al., 2017) and drought tolerance is more closely linked to water-use efficiency and hydraulic traits (Peters et al., 2021), whereas leaf morphological and anatomical traits, such as LT, LA, and LMA, vary primarily in response to local microhabitat conditions and selection pressures (Pérez et al., 2014; Li et al., 2022). Although earlier studies inferred associations between traits such as LT, LA, and LMA and drought tolerance based on πtlp, these associations were generally weak (Maréchaux et al., 2015; McGregor et al., 2021). Particularly in tropical rainforests, it is difficult to accurately infer plant drought tolerance solely from leaf morphology and anatomy (Maréchaux et al., 2020).
5. ConclusionsIn summary, karst species exhibit greater drought tolerance but lower heat tolerance than non-karst species—a pattern driven primarily by lithological differences between habitats. In contrast, similar cold tolerance across both forest types appears to result from phylogenetic constraints. Both groups display synergistic adaptations to multiple stressors, with heat tolerance closely linked to leaf structural investment. These findings highlight the vulnerability of karst plants to increasing heat extremes under climate change, emphasizing the urgent need to prioritize the conservation of these unique habitats, especially given their high endemic species richness. Future work should focus on elucidating the interplay of multiple stress factors and trait-based synergies, delving into the physiological and molecular mechanisms underlying their stress responses, thereby improving our ability to predict their persistence under future climate change.
AcknowledgementsWe are grateful to Guimin Zhao (Nonggang Nature Reserve), Hong Zeng and Youyu Chen (Guizhou University), Kexin Feng (Central South University of Forestry and Technology), and Chengkun Yang (Guilin University of Technology) for their assistance in field sampling and determination. This work was supported by the National Key Research and Developmental Program of China (2022YFE0127600), the National Natural Science Foundation of China (No. 32201286), the Guangxi Province Science and Technology Project (No. 2024JJB130016), the Hunan Provincial Natural Science Foundation of China (2025JJ20034).
CRediT authorship contribution statement
Qiufeng Ning: Conceptualization, Methodology, Formal analysis, Investigation, Writing— Original Draft. Hui Liu: Writing—Review & Editing. Jiawei LI: Writing—Review & Editing. Yunpeng Nie: Resources, Funding acquisition. Yin Wen: Conceptualization, Methodology, Resources, Writing—Review & Editing, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. All authors have approved the submission of this manuscript to Plant Diversity.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.pld.2025.08.006.
Afzal, A., Duiker, S.W., Watson, J.E., 2017. Leaf thickness to predict plant water status. Biosyst. Eng., 156: 148-156. DOI:10.1016/j.biosystemseng.2017.01.011 |
Allen, C.D., Macalady, A.K., Chenchouni, H., et al., 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag., 259: 660-684. DOI:10.1016/j.foreco.2009.09.001 |
Arnold, P.A., Briceño, V.F., Gowland, K.M., et al., 2021. A high-throughput method for measuring critical thermal limits of leaves by chlorophyll imaging fluorescence. Funct. Plant Biol., 48: 634-646. DOI:10.1071/fp20344 |
Ayala-Jacobo, L.M., Woeste, K.E., Jacobs, D.F., 2021. Cold acclimation increases freeze tolerance in Acacia koa, a tropical tree species occurring over a wide elevational gradient. Forests, 12: 1089. DOI:10.3390/f12081089 |
Bansal, S., Harrington, C.A., St Clair, J.B., 2016. Tolerance to multiple climate stressors: a case study of Douglas-fir drought and cold hardiness. Ecol. Evol., 6: 2074-2083. DOI:10.1002/ece3.2007 |
Bartlett, M.K., Klein, T., Jansen, S., et al., 2016. The correlations and sequence of plant stomatal, hydraulic, and wilting responses to drought. Proc. Natl. Acad. Sci. U.S.A., 113: 13098-13103. DOI:10.1073/pnas.1604088113 |
Bartlett, M.K., Scoffoni, C., Ardy, R., et al., 2012. Rapid determination of comparative drought tolerance traits: using an osmometer to predict turgor loss point. Methods Ecol. Evol., 3: 880-888. DOI:10.1111/j.2041-210X.2012.00230.x |
Bartlett, M.K., Zhang, Y., Kreidler, N., et al., 2014. Global analysis of plasticity in turgor loss point, a key drought tolerance trait. Ecol. Lett., 17: 1580-1590. DOI:10.1111/ele.12374 |
Blomberg, S.P., Garland Jr, T., Ives, A.R., 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution, 57: 717-745. DOI:10.1111/j.0014-3820.2003.tb00285.x |
Bray, E.A., 1997. Plant responses to water deficit. Trends Plant Sci., 2: 48-54. DOI:10.1016/S1360-1385(97)82562-9 |
Cai, Y.L., 1997. Ecological and socio-economic rehabilitation in the karst of Southwest China. J. Chin. Geogr., 7: 24-32. |
Cavender-Bares, J., Cortes, P., Rambal, S., et al., 2005. Summer and winter sensitivity of leaves and xylem to minimum freezing temperatures: a comparison of co-occurring Mediterranean oaks that differ in leaf lifespan. New Phytol., 168: 597-612. DOI:10.1111/j.1469-8137.2005.01555.x |
Chaves, M.M., Pereira, J.S., Maroco, J., et al., 2002. How plants cope with water stress in the field? Photosynthesis and growth. Ann. Bot., 89: 907. DOI:10.1093/aob/mcf105 |
Cheema, A., Garg, N., 2021. Extreme temperature-induced osmotic and oxidative stress: defensive role of organic solutes and antioxidants. In: Abdel Latef, A.A. H. (Ed.), Organic Solutes, Oxidative Stress, and Antioxidant Enzymes Under Abiotic Stressors. CRC Press, Boca Raton, pp. 263–284.
|
Chen, Y.J., Cao, K.F., Schnitzer, S.A., et al., 2015. Water-use advantage for lianas over trees in tropical seasonal forests. New Phytol., 205: 128-136. DOI:10.1111/nph.13036 |
De La Riva, E.G., Olmo, M., Poorter, H., et al., 2016. Leaf mass per area (LMA) and its relationship with leaf structure and anatomy in 34 Mediterranean woody species along a water availability gradient. PLoS One, 11: e0148788. DOI:10.1371/journal.pone.0148788 |
Desdevises, Y., Legendre, P., Azouzi, L., et al., 2003. Quantifying phylogenetically structured environmental variation. Evolution, 57: 2647-2652. DOI:10.1111/j.0014-3820.2003.tb01508.x |
Du, H., Fu, W., Song, T., et al., 2023. Water-use efficiency in a humid karstic forest in southwestern China: interactive responses to the environmental drivers. J. Hydrol., 617: 128973. DOI:10.1016/j.jhydrol.2022.128973 |
Feeley, K., Martinez-Villa, J., Perez, T., et al., 2020. The thermal tolerances, distributions, and performances of tropical montane tree species. Front. For. Glob. Change 3, 25.
|
Fu, P.-L., Zhu, S.-D., Zhang, J.-L., et al., 2019. The contrasting leaf functional traits between a karst forest and a nearby non-karst forest in south-west China. Funct. Plant Biol., 46: 907-915. DOI:10.1071/fp19103 |
Geange, S.R., Arnold, P.A., Catling, A.A., et al., 2021. The thermal tolerance of photosynthetic tissues: a global systematic review and agenda for future research. New Phytol., 229: 2497-2513. DOI:10.1111/nph.17052 |
Gill, S.S., Tuteja, N., 2010. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol. Biochem., 48: 909-930. DOI:10.1016/j.plaphy.2010.08.016 |
Grossman, J.J., 2023. Phenological physiology: seasonal patterns of plant stress tolerance in a changing climate. New Phytol., 237: 1508-1524. DOI:10.1111/nph.18617 |
Guan, N., Cheng, J., Shi, X., 2023. Preferential flow and preferential path characteristics of the typical forests in the Karst Region of Southwest China. Forests, 14: 1248. DOI:10.3390/f14061248 |
Havaux, M., 1993. Rapid photosynthetic adaptation to heat stress triggered in potato leaves by moderately elevated temperatures. Plant Cell Environ., 16: 461-467. DOI:10.1111/j.1365-3040.1993.tb00893.x |
Hawkins, B.A., Rueda, M., Rangel, T.F., et al., 2014. Community phylogenetics at the biogeographical scale: cold tolerance, niche conservatism and the structure of N orth A merican forests. J. Biogeogr., 41: 23-38. DOI:10.1111/jbi.12171 |
Jiao, W., Wang, L., Smith, W.K., et al., 2021. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun., 12: 3777. DOI:10.1038/s41467-021-24016-9 |
Jones, H.G., 2014. Plants and Microclimate: a Quantitative Approach to Environmental Plant Physiology. Cambridge University Press, Cambridge.
|
Kalaji, H.M., Schansker, G., Ladle, R.J., et al., 2014. Frequently asked questions about in vivo chlorophyll fluorescence: practical issues. Photosynth. Res., 122: 121-158. DOI:10.1007/s11120-014-0024-6 |
Kikstra, J.S., Nicholls, Z.R., Smith, C.J., et al., 2022. The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures. Geosci. Model Dev., 15: 9075-9109. DOI:10.5194/gmd-15-9075-2022 |
Knaupp, M., Mishra, K.B., Nedbal, L., et al., 2011. Evidence for a role of raffinose in stabilizing photosystem Ⅱ during freeze–thaw cycles. Planta, 234: 477-486. DOI:10.1007/s00425-011-1413-0 |
Krause, G.H., Winter, K., Krause, B., et al., 2010. High-temperature tolerance of a tropical tree, Ficus insipida: methodological reassessment and climate change considerations. Funct. Plant Biol., 37: 890-900. DOI:10.1071/FP10034 |
Kröber, W., Heklau, H., Bruelheide, H., 2015. Leaf morphology of 40 evergreen and deciduous broadleaved subtropical tree species and relationships to functional ecophysiological traits. Plant Biol., 17: 373-383. DOI:10.1111/plb.12250 |
Lancaster, L.T., Humphreys, A.M., 2020. Global variation in the thermal tolerances of plants. Proc. Natl. Acad. Sci. U.S.A., 117: 13580-13587. DOI:10.1073/pnas.1918162117 |
Leigh, A., Sevanto, S., Ball, M.C., et al., 2012. Do thick leaves avoid thermal damage in critically low wind speeds?. New Phytol., 194: 477-487. DOI:10.1111/j.1469-8137.2012.04058.x |
Leigh, A., Sevanto, S., Close, J., et al., 2017. The influence of leaf size and shape on leaf thermal dynamics: does theory hold up under natural conditions?. Plant Cell Environ., 40: 237-248. DOI:10.1111/pce.12857 |
Levionnois, S., Jansen, S., Wandji, R.T., et al., 2021. Linking drought-induced xylem embolism resistance to wood anatomical traits in Neotropical trees. New Phytol., 229: 1453-1466. DOI:10.1111/nph.16942 |
Li, J., Guo, Y., Li, D., et al., 2023. Spatial distribution characteristics of soil potassium, calcium, and magnesium and their influencing factors in a northern tropical karst seasonal rainforest in southwestern Guangxi. Biodivers. Sci., 31: 22352. DOI:10.17520/biods.2022352 |
Li, Y., Liu, C., Sack, L., et al., 2022. Leaf trait network architecture shifts with species-richness and climate across forests at continental scale. Ecol. Lett., 25: 1442-1457. DOI:10.1111/ele.14009 |
Liu, H., Ye, Q., Lundgren, M.R., et al., 2024. Phylogeny and climate explain contrasting hydraulic traits in different life forms of 150 woody Fabaceae species. J. Ecol., 112: 741-754. DOI:10.1111/1365-2745.14266 |
Liu, T., Li, Y., Duan, W., et al., 2017. Cold acclimation alters DNA methylation patterns and confers tolerance to heat and increases growth rate in Brassica rapa. J. Exp. Bot., 68: 1213-1224. DOI:10.1093/jxb/erw496 |
Lobo, A., Torres-Ruiz, J.M., Burlett, R., et al., 2018. Assessing inter-and intraspecific variability of xylem vulnerability to embolism in oaks. For. Ecol. Manag., 424: 53-61. DOI:10.1016/j.foreco.2018.04.031 |
Lv, Y., Zhang, L., Li, P., et al., 2023. Ecological restoration projects enhanced terrestrial carbon sequestration in the karst region of Southwest China. Front. Ecol. Evol., 11: 1179608. DOI:10.3389/fevo.2023.1179608 |
Ma, L., Chao, L., He, Y.-S., et al., 2024. Relationship of embolism resistance with xylem anatomical structure and related traits of 12 tree species in tropical karst seasonal rainforests. Chine. J. Plant Ecol., 48: 888. DOI:10.1002/ohn.810 |
Marchin, R.M., Backes, D., Ossola, A., et al., 2022. Extreme heat increases stomatal conductance and drought-induced mortality risk in vulnerable plant species. Glob. Change Biol., 28: 1133-1146. DOI:10.1111/gcb.15976 |
Maréchaux, I., Bartlett, M.K., Sack, L., et al., 2015. Drought tolerance as predicted by leaf water potential at turgor loss point varies strongly across species within an Amazonian forest. Funct. Ecol., 29: 1268-1277. DOI:10.1111/1365-2435.12452 |
Maréchaux, I., Saint-André, L., Bartlett, M.K., et al., 2020. Leaf drought tolerance cannot be inferred from classic leaf traits in a tropical rainforest. J. Ecol., 108: 1030-1045. DOI:10.1111/1365-2745.13321 |
McGregor, I.R., Helcoski, R., Kunert, N., et al., 2021. Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest. New Phytol., 231: 601-616. DOI:10.1111/nph.16996 |
Münchinger, I.K., Hajek, P., Akdogan, B., et al., 2023. Leaf thermal tolerance and sensitivity of temperate tree species are correlated with leaf physiological and functional drought resistance traits. J. For. Res., 34: 63-76. DOI:10.1007/s11676-022-01594-y |
Nievola, C.C., Carvalho, C.P., Carvalho, V., et al., 2017. Rapid responses of plants to temperature changes. Temperature (Austin), 4: 371-405. DOI:10.1080/23328940.2017.1377812 |
Niinemets, Ü., Valladares, F., 2006. Tolerance to shade, drought, and waterlogging of temperate northern hemisphere trees and shrubs. Ecol. Monogr., 76: 521-547. |
Ning, Q.-R., Li, Q., Zhang, H.-P., et al., 2024. Weak correlations among leaf thermal metrics, economic traits and damages under natural heatwaves. Sci. Total Environ., 916: 170022. DOI:10.1016/j.scitotenv.2024.170022 |
O'sullivan, O.S., Heskel, M.A., Reich, P.B., et al., 2017. Thermal limits of leaf metabolism across biomes. Glob. Change Biol., 23: 209-223. DOI:10.1111/gcb.13477 |
Pérez, F., Hinojosa, L.F., Ossa, C.G., et al., 2014. Decoupled evolution of foliar freezing resistance, temperature niche and morphological leaf traits in Chilean Myrceugenia. J. Ecol., 102: 972-980. DOI:10.1111/1365-2745.12261 |
Perez, T.M., Feeley, K.J., 2021. Weak phylogenetic and climatic signals in plant heat tolerance. J. Biogeogr., 48: 91-100. DOI:10.1111/jbi.13984 |
Perez, T.M., Socha, A., Tserej, O., et al., 2021. Photosystem II heat tolerances characterize thermal generalists and the upper limit of carbon assimilation. Plant Cell Environ., 44: 2321-2330. DOI:10.1111/pce.13990 |
Peters, J.M., López, R., Nolf, M., et al., 2021. Living on the edge: a continental-scale assessment of forest vulnerability to drought. Glob. Change Biol., 27: 3620-3641. DOI:10.1111/gcb.15641 |
Pinheiro, C., Chaves, M.M., 2011. Photosynthesis and drought: can we make metabolic connections from available data?. J. Exp. Bot., 62: 869-882. DOI:10.1093/jxb/erq340 |
Puglielli, G., Laanisto, L., Gori, A., et al., 2023. Woody plant adaptations to multiple abiotic stressors: where are we?. Flora, 299: 152221. |
Qi, Y., Zhang, G., Luo, G., et al., 2021. Community-level consequences of harsh environmental constraints based on spatial patterns analysis in karst primary forest of southwest China. For. Ecol. Manag., 488: 119021. |
Sastry, A., Barua, D., 2017. Leaf thermotolerance in tropical trees from a seasonally dry climate varies along the slow-fast resource acquisition spectrum. Sci. Rep., 7: 11246. |
Sastry, A., Guha, A., Barua, D., 2018. Leaf thermotolerance in dry tropical forest tree species: relationships with leaf traits and effects of drought. AoB Plants, 10: plx070. |
Sato, H., Mizoi, J., Shinozaki, K., et al., 2024. Complex plant responses to drought and heat stress under climate change. Plant J., 117: 1873-1892. DOI:10.1111/tpj.16612 |
Seddon, A.W., Macias-Fauria, M., Long, P.R., et al., 2016. Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531: 229-232. DOI:10.1038/nature16986 |
Sierra-Almeida, A., Cavieres, L.A., Bravo, L.A., 2009. Freezing resistance varies within the growing season and with elevation in high-andean species of central Chile. New Phytol., 182: 461-469. DOI:10.1111/j.1469-8137.2008.02756.x |
Slot, M., Cala, D., Aranda, J., et al., 2021. Leaf heat tolerance of 147 tropical forest species varies with elevation and leaf functional traits, but not with phylogeny. Plant Cell Environ., 44: 2414-2427. DOI:10.1111/pce.14060 |
Stahl, U., Kattge, J., Reu, B., et al., 2013. Whole-plant trait spectra of North American woody plant species reflect fundamental ecological strategies. Ecosphere, 4: 1-28. DOI:10.1890/es13-00143.1 |
Stahl, U., Reu, B., Wirth, C., 2014. Predicting species' range limits from functional traits for the tree flora of North America. Proc. Natl. Acad. Sci. U.S.A., 111: 13739-13744. DOI:10.1073/pnas.1300673111 |
Tang, S., Liu, J., Lambers, H., et al., 2021. Increase in leaf organic acids to enhance adaptability of dominant plant species in karst habitats. Ecol. Evol., 11: 10277-10289. DOI:10.1002/ece3.7832 |
Tserej, O., Feeley, K.J., 2021. Variation in leaf temperatures of tropical and subtropical trees are related to leaf thermoregulatory traits and not geographic distributions. Biotropica, 53: 868-878. DOI:10.1111/btp.12919 |
Valliere, J.M., Nelson, K.C., Martinez, M.C., 2023. Functional traits and drought strategy predict leaf thermal tolerance. Conserv. Physiol., 11: coad085. |
Wan, C.-Y., Yu, J.-R., Li, Z.-G., et al., 2025. Contrasting leaf nutrient-hydraulic relationships between karst and non-karst forests. Plant Soil: 1-13. DOI:10.1007/s11104-025-07217-9 |
Wan, C. -Y., Yu, J. -R., Zhu, S. -D., 2023. Differences in leaf traits and trait correlation networks between karst and non-karst forest tree species. Chin. J. Plant Ecol., 47: 1386-1397. DOI:10.17521/cjpe.2022.0469 |
Wang, C., Li, Z., Chen, Y., et al., 2023a. Drought-heatwave compound events are stronger in drylands. Weather Clim. Extrem., 42: 100632. |
Wang, H., Prentice, I.C., Wright, I.J., et al., 2023b. Leaf economics fundamentals explained by optimality principles. Sci. Adv., 9(3): eadd5667. |
Wang, J., Wen, X., Zhang, X., et al., 2018. Co-regulation of photosynthetic capacity by nitrogen, phosphorus and magnesium in a subtropical Karst forest in China. Sci. Rep., 8: 7406. |
Wen, Y., Qin, D.-w., Leng, B., et al., 2018. The physiological cold tolerance of warm-climate plants is correlated with their latitudinal range limit. Biol. Lett., 14: 20180277. DOI:10.1098/rsbl.2018.0277 |
Wen, Y., Ye, Q., Román-Palacios, C., et al., 2023. Physiological cold tolerance evolves faster than climatic niches in plants. Front. Plant Sci., 14: 1257499. |
Wilcox, B.P., Taucer, P.I., Munster, C.L., et al., 2008. Subsurface stormflow is important in semiarid karst shrublands. Geophys. Res. Lett., 35: L10403. |
Wright, I.J., Dong, N., Maire, V., et al., 2017. Global climatic drivers of leaf size. Science, 357: 917-921. DOI:10.1126/science.aal4760 |
Wright, I.J., Reich, P.B., Westoby, M., et al., 2004. The worldwide leaf economics spectrum. Nature, 428: 821-827. |
Yao, C.-H., Jiang, Z.-C., Yuan, D.-X., 2001. Vegetation karst effects on the karst area of southwest China. Acta Geosci. Sin., 22: 159-164. |
Ye, J.-H., Yu, C.-L., Zhuo, S.-F., et al., 2023. Correlations of photosynthetic heat tolerance with leaf morphology and temperature niche in Magnoliaceae. Chin. J. Plant Ecol., 47: 1432. DOI:10.17521/cjpe.2022.0298 |
Zandalinas, S.I., Fritschi, F.B., Mittler, R., 2021. Global warming, climate change, and environmental pollution: recipe for a multifactorial stress combination disaster. Trends Plant Sci., 26: 588-599. |
Zhang, F., Liu, Y.W., Qin, J., et al., 2024a. Xylem embolism induced by freeze–thaw and drought are influenced by different anatomical traits in subtropical montane evergreen angiosperm trees. Physiol. Plantarum, 176: e14567. |
Zhang, H., Ning, Q., Li, Q., et al., 2024b. Contrasting heat tolerance of evergreen and deciduous urban woody species during heat waves. Funct. Ecol., 38: 1649-1660. DOI:10.1111/1365-2435.14562 |
Zhang, L., Du, H., Yang, Z., et al., 2022. Topography and soil properties determine biomass and productivity indirectly via community structural and species diversity in karst forest, Southwest China. Sustainability, 14: 7644. |
Zhou, Y.-Y., Lin, H., 2023. Variation of leaf thermal traits and plant adaptation strategies of canopy dominant tree species along temperature and precipitation gradients. Chin. J. Plant Ecol., 47: 733-744. DOI:10.17521/cjpe.2022.0289 |