Leaf Nitrogen and Phosphorus Stoichiometry and Its Response to Geographical and Climatic Factors in a Tropical Region: Evidence from Hainan Island
by
Jingjing Wang
1,2,†, 
Yongyi Liang
1,3,†,
Guoan Wang
4,
Xiaoyan Lin
1,3,
Jiexi Liu
1,5,
Hao Wang
1,6,
Zixun Chen
4,* and
Bingsun Wu
2,3,* 1
School of Forestry, Hainan University, Haikou 570228, China
2
Opening Project Fund of Key Laboratory of Biology and Genetic Resources of Rubber Tree/State Key Laboratory Breeding Base of Cultivation and Physiology for Tropical Crops/Danzhou Investigation and Experiment Station of Tropical Crops, Ministry of Agriculture and Rural Affairs, Danzhou 571737, China
3
Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
4
Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, Department of Environmental Sciences and Engineering, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
5
National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Institutes of Plant Physiology and Ecology, Shanghai 200032, China
6
College of International Studies, Yangzhou University, Yangzhou 225009, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(2), 411; https://doi.org/10.3390/agronomy13020411
Submission received: 4 January 2023
/
Revised: 20 January 2023
/
Accepted: 25 January 2023
/
Published: 30 January 2023
(This article belongs to the Special Issue Cultivated Land Sustainability in the Anthropocene)
Abstract
:Leaf stoichiometry effectively indicates the response and adaptation of plants to environmental changes. Although numerous studies on leaf stoichiometry patterns have focused on the mid-latitudes and specific species of plants, these patterns and the effect of the climate change on them across a broad range of plants have remained poorly characterized in hot and humid regions at low latitudes. In the present study, leaf N, P, N:P, C:N, and C:P ratios, were determined from 345 plant leaf samples of 268 species at four forest sites in Hainan Island, China. For all plants, leaf N (3.80 ± 0.20 mg g−1) and P (1.82 ± 0.07 mg g−1) were negatively correlated with latitude and mean annual temperature (MAT) but were positively correlated with longitude. Leaf N was found to be positively correlated with altitude (ALT), and leaf P was positively correlated with mean annual precipitation (MAP). The leaf C:N ratio (278.77 ± 15.86) was significantly correlated with longitude and ALT, leaf C:P ratio (390.69 ± 15.15) was significantly correlated with all factors except ALT, and leaf N:P ratio (2.25 ± 0.10) was significantly correlated with ALT, MAT, and MAP. Comparable results were observed for woody plants. The results suggest that leaf stoichiometry on Hainan Island is affected by changes in geographical and climatic factors. In addition, the low N:P ratio indicates that plant growth may be limited by N availability. Moreover, the significant correlation between leaf N and P implies a possible synergistic relationship between N and P uptake efficiency in the plants of this region. This study helps to reveal the spatial patterns of leaf stoichiometry and their response to global climate change in a variety of plants in tropical regions with hot and humid environments, which may provide an insight in nutrient management in tropical rainforest.
1. Introduction
Leaf stoichiometry can indicate plant nutrient status, community composition, and ecosystem functions, and drives fundamental physiological and ecological processes in plants [1,2]. Essential nutrients for plants, such as carbon, nitrogen, and phosphorus, affect plant growth and adaptation to terrestrial habitats and are closely related to global biochemical cycles [3]. N and P are closely related to plant photosynthesis, genetic material composition, energy storage, and are the most important limiting nutrients in terrestrial ecosystems [4,5,6]. In particular, the stoichiometry of N and P is closely related to plant ecological strategies [7,8,9,10,11]. For example, as important indicators of leaf nutrient usage efficiency, higher leaf C:N and C:P ratios indicate a more efficient usage of N and P [4,6,12,13]. Research has shown that climate change considerably affects matter and energy cycles, both regionally and globally, thereby affecting vegetation activity and ecosystem function [11,14,15,16,17]. For instance, warming can affect the rate of alter litter decomposition and organic matter mineralization via changes in the soil’s physicochemical properties and microbial activity, ultimately leading to changes in plant nutrient availability and leaf stoichiometry [4,11,14]. Therefore, understanding the effects of geographical and climatic factors on the leaf N and P contents, as well as on the C:N, C:P, and N:P ratios, plays a vital role in discerning the plant response and adaptation to environmental changes.
Ecosystem functions and processes are regulated by both biotic and abiotic factors [18,19,20,21]. The former includes plant functional traits, whereas the latter includes edaphic, geographic, and climatic features. Thus, spatial variations in plant leaf chemometrics are influenced by various factors. Changes in climate and geomorphology, including air temperature, precipitation, and latitude, have significant impacts on plant physiology and soil biogeochemistry, which affects the nutrient cycling in ecosystems [22,23]. Reich and Oleksyn [4] described global patterns in leaf N and P stoichiometry of terrestrial plants across latitudinal and temperature gradients. They proposed that leaf N and P concentrations rise from the tropics to the mid-latitude regions and remain stable or decline at high-latitude regions. Additionally, they reported that the leaf N:P ratio increases with temperature [4]. Previous studies have shown that tropical climate and soil nutrient changes may lead to different spatial patterns of leaf C, N, and P stoichiometry and nutrient resorption [8,24]. Han et al. [25] analyzed leaf data from 753 terrestrial plants in China and found that the variations in leaf N and P concentrations showed an opposite trend to the mean annual temperature (MAT), but leaf N:P did not show significant changes. However, when additional species in China were considered, they found that plant functional type exhibited the greatest impact on most leaf nutrients. Additionally, the variation in leaf N and lack thereof in leaf P was better explained by changes in precipitation, rather than temperature [26]. Possibly due to the low availability of soil P in China, the previous two reports found that the leaf N:P ratio of Chinese flora was higher than the global average [25,26]. Other studies found that intense precipitation can exacerbate soil nutrient loss, resulting in reduced leaf P concentration [27].
The relationship between leaf stoichiometry and environmental factors has become a research hotspot in ecology and earth sciences [4]. The concentrations of leaf N and P can be used as indicators of how plants use nutrients and respond to environmental changes, as they are associated with many key aspects of plant growth, reproduction, and ecosystem functions [3,28]. Therefore, current studies on leaf stoichiometry mainly focus on N and P. This is especially true for studies exploring leaf stoichiometry models in the mid-latitude regions and under specific conditions [11,29,30,31,32,33,34,35]. However, leaf stoichiometry patterns of various plants in areas with elevated temperatures and humidity at low latitudes, such as tropical regions, are poorly understood, limiting understanding of plant growth strategies in these areas under severe climate change conditions.
Tropical forests are the terrestrial ecosystems with the highest biodiversity and strongest ecological functions, causing them to be very significant to the global C budget. They account for 70% and approximately 55% of the gross global forest C sink and C pool, respectively [36,37]. Hainan Island is the largest and most diverse tropical-type forest in China. Owing to their high diversity, endemism, and complexity, tropical forests on Hainan Island are of great significance at both the national and global protection levels [38]. Here, we hypothesized the leaf N and P stoichiometry patterns would be affected by geographical and climatic factors in Hainan Island with high temperature and high humidity. To test our hypothesis, we selected the four areas of Danzhou, Tunchang, Changjiang, and Wuzhishan on Hainan Island as sampling points and analyzed the leaf nutrients of 345 leaf samples from 268 species. First, this study aimed to measure the leaf N and P content of all plants at four sampling sites on Hainan Island. Next, the relationship between leaf N, P concentration, C:N, C:P, and N:P ratios; and climatic and geographical factors were analyzed. This report provides better evidence of the patterns and drivers of leaf N and P stoichiometry and nutrient uptake on Hainan Island, which is important for discovering plant growth strategies in the tropical region under drastic environmental changes, and for guiding the nutrient management in tropical rainforests.
2. Materials and Methods
2.1. Site Description
Our study was conducted at four forest sites (Wuzhishan, Danzhou, Changjiang and Tunchang) in the western central region of Hainan Island. These forest sites are geographically located from 109°2′ to 110°6′ E and 18°47′ to 19°22′ N (Figure 1). There were two plots (18°55′45.46″ N, 109°28′7.83″ E; 18°47′40.22″ N, 109°38′54.94″ E) in Wuzhshang, and only one plot in Danzhou (19°30′50.94″ N, 109°29′58.70″ E). Changjiang (19°07′21.87″ N, 109°04′45.63″ E) and Tunchang (19°27′48.29″ N, 110°05′52.77″ E). The study area is a humid tropical region, where the climate type is tropical monsoon and tropical alpine climate, with a MAT of 22 to 25 °C. The average annual temperature of Wuzhishan, Danzhou, Changjiang, and Tunchang is 22.80, 23.70, 24.33, and 23.13 °C, respectively. Mean annual precipitation (MAP) in the whole study region is 1400 to 2100 mm, with 70% to 90% of the precipitation concentrated in the rainy season from May to October. The total precipitation in the rainy season is >1500 mm. The MAP of Wuzhishan, Danzhou, Changjiang, and Tunchang is 2080.95 mm, 1934.99 mm, 1563.12 mm, and 2105.15 mm, respectively. The altitude (ALT) of the research area ranges from 135 to 660 m above sea level. The major soil types are laterite and yellow. The main soil types in Wuzhishan are yellow soil and latosol, while the main soil types in Danzhou, Changjiang and Tunchang are latosol. The dominant climate type in Wuzhishan is tropical alpine climate and in Danzhou, Changjiang, and Tunchang is tropical monsoon climate. Specific information regarding the study area is presented in Table 1.
2.2. Plant Sampling and Chemical Analysis
Leaf samples were collected from the study sites between August and September 2017. A healthy plant community was selected for each site in this study. More than three individuals from each species were selected and fully expanded healthy leaves were collected from shoots in different directions in areas of sun-exposed (total fresh mass > 100 g for each species). In total, we collected 345 leaf samples from 268 species. A total of 102 samples from different species were collected from Wuzhishan; 83 samples came from Danzhou; 83 samples were collected from Changjiang; and 77 samples were from Tunchang. Sample statistics were listed in Table 2, and the species of all samples were listed in Table A1.
All leaf samples were placed in sealed plastic bags and transported to the laboratory. The leaf samples were rinsed with distilled water before being oven-dried at 105 °C for 30 min to denature the enzymes. Next, the samples were dried at 75 °C for approximately 48 h to a consistent weight and were finely ground. Leaf N and P concentrations were determined after sample digestion in H2SO4-H2O2, using a flow analyzer (Proxima1022/1/1, Alliance, France).
2.3. Accessing Data
The MAT, MAP, and other meteorological data of Hainan Island from 1959 to 2019 were obtained from the National Meteorological Science Data Center (Beijing, China). For research areas lacking climate data, the Inverse Distance Weighted method was used to fit the spatial variation map of Hainan climate data according to data from the Hainan Island meteorological station, producing climate data of the research area. Additionally, leaf C concentration data were obtained in another part of this project, a report on “Effects of geographical and climatic factors on the intrinsic water use efficiency of tropical plants: evidence from leaf 13C” (unpublished results).
2.4. Statistical Analysis
The inverse distance weight interpolation method of ArcGIS 10.6 was used to obtain the climatic data from each study site from 1959 to 2019. IBM SPSS Statistics 25 was used to conduct single-factor analysis of variance and Spearman correlation analysis.
3. Results
3.1. Leaf Stoichiometry Characteristics in Hainan Island
In this study, the mean leaf N and P concentrations were 3.80 and 1.82 mg g−1 respectively, ranging from 0.16 to 16.39 mg g−1 and 0.24 to 7.18 mg g−1, respectively. The coefficient of variation (CV) for leaf N and P concentrations ranged from 5.36 to 3.85, in which the leaf N concentration CV was the highest (Table 3). In this study, there was a significant positive correlation between leaf N and P concentrations in Hainan Island. (p < 0.01; Figure 2). The average leaf C:N, C:P, and N:P ratios and ranges can be found in Table 3.
3.2. Variations in Leaf Stoichiometry alongside Geographical and Climatic Variables
At the spatial scale, both leaf N and P concentrations decreased with latitude, and the C:N and C:P ratios increased with latitude (p < 0.01, Figure 3a,b), whereas the leaf N:P ratio did not change with latitude (Figure 3c). With increasing longitude, both leaf N (p < 0.05) and P (p < 0.01) concentrations increased, but the C:P ratio decreased (p < 0.01, Figure 3d,e). The C:N and N:P ratios did not change with longitude (Figure 3e,f). The leaf N concentration and N:P ratio (Figure 3g,i) significantly increased with altitude (p < 0.05, and p < 0.01, respectively). Meanwhile, the leaf C:N ratio decreased with increasing ALT, and the leaf P concentration (p < 0.01, Figure 3g,h) and C:P ratio (Figure 3h) showed no marked changes along ALT.
The leaf P concentration increased, and the C:P and N:P ratios decreased with increasing MAP; however, the leaf N concentration and C:N and N:P ratios did not change with MAP (p < 0.01, Figure 4a–c). Both leaf N and P concentrations decreased with increasing MAT; however, the C:P and N:P ratios increased with increasing MAT, whereas the leaf C:N ratio was not affected by MAT (p < 0.01, Figure 4d–f).
3.3. Characteristics of Leaf Stoichiometry among Different Life Forms
There was no significant difference (p < 0.05) in the N concentration in the leaf of different life forms. The leaf N concentration of each life form was in the following order: herbs (4.34 mg g−1), woody plants (3.73 mg g−1), and vines (2.94 mg g−1). In contrast, the leaf P concentration of herbs (2.35 mg g−1) was significantly higher than that of woody plants and vines (1.68 and 1.54 mg g−1, respectively) (p < 0.05). There were no significant differences in leaf P content among the remaining life forms (Figure 5a, p < 0.05).
Among the different life forms, the C:N ratio was the highest in woody plants, followed by vines and herbs; however, there were no significant differences among the ratios of the different life forms. The C:P ratio in herb leaves was significantly lower than that in woody plants (p < 0.05). In descending order, the C:P ratio was the highest in woody plants, vines, and herbs (Figure 5c). The N:P ratio was significantly higher in the leaves of woody plants than in herbs (p < 0.05). No significant differences were observed between the ratios of the leaves of the other life forms. In descending order, the N:P ratio was the highest in woody plants, vines, and herbs (Figure 5b).
3.4. Leaf Stoichiometry in Different Life Forms Response to Environmental Factors
3.4.1. Variations in Leaf Stoichiometry in Different Life Forms: Geographical Variables
The average leaf N and P concentrations in woody plant leaves are negative correlated with latitude, whereas the average leaf N and P concentrations of herbs and vines were not significantly correlated with latitude (p < 0.05). Both leaf N and P concentrations of woody plants showed a positive correlation with longitude. Meanwhile, the average leaf N and P concentrations of herbs and vines were not significantly correlated with longitude (p < 0.05). The average leaf N concentrations of woody plants showed a positive correlation with ALT, whereas the other plant life forms and their elemental concentrations had no significant correlation with this parameter (Figure 6, p < 0.05).
The average leaf C:N and C:P ratios of woody plants showed a positive correlation with latitude, whereas the ratios of herb and vine leaves were not significantly correlated with latitude (p < 0.05). The average leaf C:P ratio of woody plants and average leaf N:P ratio of vines showed a negative correlation with longitude, whereas the average leaf C:N ratio of herbs showed a positive correlation with longitude. No significant correlations were found between the stoichiometric ratios of the other life forms and longitude. The average leaf C:P ratio of woody plants and herbs showed a negative correlation with altitude, whereas the average leaf N:P ratio of woody plants and herbs showed a positive correlation with ALT. The stoichiometric ratios of other life forms were not significantly correlated with the change in ALT levels (Figure 6, p < 0.05).
3.4.2. Variations in Leaf Stoichiometry in Different Life Forms: Climatic Variables
The average leaf N concentration of woody plants showed a positive correlation with MAP, whereas the average leaf N and P concentrations of herbs and vines were not significantly correlated with MAP (p < 0.05). In terms of temperature variation, the average leaf N and P concentrations of woody plants were negatively correlated with MAT, whereas the average leaf N and P concentrations of herbs and vines were not significantly correlated with MAT (Figure 6, p < 0.05).
The average leaf C:P ratio of woody plants and average leaf N:P ratio of herbs and vines were negatively correlated with MAP, whereas the stoichiometric ratios of the other life forms were not significantly correlated with MAP (p < 0.05). The average leaf C:P ratio of woody plants, C:P and N:P ratios of herbs, and N:P ratio of vines were positively correlated with MAT. The stoichiometric ratios of the other life forms were not significantly correlated with MAT (Figure 6, p < 0.05).
4. Discussion
4.1. Patterns of Leaf Stoichiometry in Hainan Island
Leaf stoichiometry is used as an important indicator to study plant nutrient limitation, nutrient cycling, and plant response to climate change [39,40]. The present study showed that the average leaf N concentration of 268 species on Hainan Island was 3.80 mg g−1 (Table 3), which was lower than that reported in global and other regional scale [4,25,41]. Compared with other regions, higher precipitation and temperature in Hainan Island may promote enzymatic activity and photosynthesis, thereby accelerating nutrient cycling and leading to relatively lower leaf N concentrations [42]. In addition, evergreen woody plants accounted for more than two-thirds of the total plant samples in this study (Table 2 and Table A1). Lower N concentrations in evergreen species is suggested to facilitate the adaptation to a wide range of conditions in different habitats [43]. Moreover, there is tight coupling between soil and plant nutrients [44]. Soil acidification is evident on Hainan Island [45], which inhibits microbial activity and the decomposition of organic matter, slowing the release of soil nutrients and thus affecting the uptake of soil N nutrients by plants. The mean leaf P concentration in Hainan Island was 1.82 mg g−1, which was slightly higher than that reported in previous studies [4,25,41]. Different from soil-available nitrogen, which comes from decomposition of organic matter, soil-available phosphorus is mainly derived from the weathering of rocks [46,47]. In the tropics and subtropics, geochemical and biological processes are expected to occur at faster rates, resulting in intense soil weathering [48,49,50]. Previous studies have shown that the soil P concentration tends to increase, and the N:P ratio tends to decrease on Hainan Island [51]. In addition, enhanced precipitation can increase the soil P uptake by plants [47,52]. Consequently, leaf P concentrations of plants in our study were higher than those in previous studies. The average leaf C:N and C:P ratios were 278.77 and 390.60, respectively (Table 3), which were higher than those in global scale [4,53]. The suitable moisture and temperature conditions in Hainan Island may accelerate the photosynthetic C assimilation in plants, resulting in higher N, P utilization, and thus higher C:N and C:P ratios [23,54]. The average leaf N:P ratio was 2.25, which was lower than global research [4]. The average leaf P concentration in this study was slightly higher than that in previous studies, whereas the leaf N concentration was lower, causing the lower N:P ratio in Hainan Island.
For plants of different life forms, the average leaf N and P concentrations of the herbs were the highest. According to the growth rate hypothesis [55,56], leaf N and P concentrations in short-lived and fast-growing species (e.g., annual herbaceous plants) are always higher than those in long-lived and slow-growing species (e.g., evergreen woody plants). Herbs have a shorter life span than woody plants [57,58]; therefore, they have higher leaf N and P concentrations. The homeostasis system of herbs is weaker than that of vines, resulting in a more quickly stoichiometric change under environmental shifts, and thus higher leaf N and P concentrations.
The stoichiometric ratio can objectively reflect the distribution and trade-offs of the restrictive elements of the plant during the growth process [59,60]. A previous study suggested that the C:N, C:P, and N:P ratios play a significant role in the determination of the plant nutrient limitation [61]. According to Verhoeven et al. [62], when N:P is less than 14, plant growth is mainly restricted by N; meanwhile, N:P greater than 16 results in the restriction of plant growth mainly by P. As mentioned above, the average leaf N:P ratio of the 268 plants in this study was 2.25, suggesting that plant growth on Hainan Island may be limited by N. This conclusion has also been proved by some previous studies [51,63]. N limitation is widespread among different habitats [64]. According to our results, N is also a key factor limiting plant growth in temperate and tropical forests. In addition, there was a close link between leaf N and P concentrations (Figure 2), which is consistent with several previous studies conducted at national and global scales [4,25]. This result suggests that there may be a synergistic relationship between the N and P absorption efficiency of plants on Hainan Island [65].
4.2. Influence of Geographical and Climatic Factors on Leaf Stoichiometry
The present study found that leaf N, P stoichiometry had significant correlation with latitude, longitude, altitude, MAT and MAP, which confirmed our hypothesis that leaf N and P stoichiometry patterns in Hainan Island would be affected by geographical and climatic factors. Changes in temperature and precipitation can affect plant growth and nutrient metabolism, consequently affecting the nutrient cycling of ecosystems [20,25,65,66]. The leaf N and P concentrations were significantly negatively correlated with MAT (Figure 4d), which were also observed in mainland China and on a global scale [4,25,41]. The temperature–plant physiology hypothesis [4] suggests that due to physiological acclimation (i.e., plants regulate N, P levels to counteract the effects of temperature) and the adaptation to temperature (i.e., temperature regulates N, P levels by affecting plant metabolism), N and P decline monotonically with increasing temperature. In general, temperature decreases with increasing latitude, resulting in a positive relationship between leaf N, P concentrations and latitude [4,25,41]. However, a negative correlation has been found between leaf N, P concentrations and latitude in Hainan Island (Figure 3a). This may be because the latitudinal range of our study area (18.79° to 19.51° N, Table 1) is smaller than those of the previous studies in global scale (43 to 70° N) [4], the Chinese mainland (18° to 48° N) [25], and the north–south transect of eastern China (18° to 52° N) [41]. At a smaller gradient, the leaf N and P concentrations showed weak geographical patterns and even decreased with latitude [3,67,68]. C:N and C:P ratios are important physiological indices of plants growth rate [56,69,70]. Our results showed that the leaf C:N and C:P ratios increased with latitude and MAT (Figure 3b and Figure 4b), implying that nutrient utilization and C assimilation rates increased in high-latitude regions [11,23,54,71]. Leaf N:P ratios reflect the relative availability of N [72]. Owing to the limited latitudinal range of the study area, no significant correlation between leaf N:P and latitude was observed (Figure 3f), this indicates that N availability does not vary with latitudinal gradient.
In this study, the leaf P concentration showed a significantly positive correlation with MAP (Figure 4a), which was consistent with the results of Sardans et al. [73]. High precipitation may enhance the nutrient uptake capacity of plants [74,75,76], resulting in a positive relationship between leaf P concentration and MAP. However, there was no significant correlation between leaf N concentration and MAP (Figure 4a), which differs from the results of a previous report [25]. This may be caused by the high nitrogen deposition in China over the last 30 years [6,77,78]. N deposition exacerbates the nutrient imbalance and disturb the C, N, and P cycles in tropical ecosystem [79]. A study in a tropical forest in China showed that large amounts of reactive atmospheric N deposition were absorbed and transported into plant tissues [80], which might have led to weak relationships between the leaf N concentration and MAP. In addition, Hui et al. [51] showed that the soil N availability on Hainan Island was lower, which might be due to the leaching of N modulated by the high annual precipitation. Therefore, the impact of soil N availability on the leaf N content on Hainan Island may be higher than the effect of MAP, resulting in the observed insignificance between leaf N concentration and MAP. The leaf N and P concentrations were positively correlated with longitude (Figure 3d), which is consistent with the findings of Han et al. [26]. The distribution of precipitation in China gradually decreases from the southeast coast to the northwest inland region. Therefore, the longitudinal zonality of leaf stoichiometry in China is mainly affected by precipitation. The ratio of leaf C:P and N:P in leaf are vital indicators of plant growth because the distribution and variation in P-rich RNA occur at different growth rates [55,81,82]. In our study, the leaf C:P and N:P ratios decreased with increasing MAP, which may have been influenced by the relationship between leaf N and P concentrations and climate (Figure 4a–c). These correlations indicate that along with longitude, high MAP promotes the utilization efficiency of P, improving the growth rate of plants [4,83].
The leaf N concentration and N:P ratio in Hainan Island were significantly positively associated with the altitude (Figure 3g,i), whereas the trend of the C:N ratio exhibited the opposite behavior (Figure 3h), and there was no significant correlation between P concentration and altitude, and between C:P and altitude (Figure 3g,i). Climatic and soil factors change along the altitudinal gradients, leading to the variation in plant functional traits and nutrient composition [84,85,86,87]; thereby, leaf stoichiometry changes with altitude [88,89,90,91,92,93,94]. Temperature decreases monotonically with increasing altitude, and leaf N concentration has a negative relationship with temperature. Therefore, leaf N, even N:P ratio increased, and C:N ratio decreased with increasing altitude in Hainan Island. No correlation between leaf P concentration and altitude, and between C:P ratio and altitude may be associated with the disturbance of soil phosphorus availability, which may also change along altitude.
In order to reduce interspecific competition [24], plants of different life forms have different resource utilization efficiencies and environmental adaptation strategies. Therefore, leaf element concentrations and their correlation with geographical and climatic factors change across life forms. The leaf stoichiometric characteristics of woody plants were consistent with those of the entire study area, whereas the leaf N and P concentrations and stoichiometric ratios of herbs and vine were almost not significantly related to geographical and climatic factors (Figure 6a,b). Limited by relatively shallow root depth more than woody plants, nutrient state in herbs is more sensitive to the change in soil nutrient availability. Thereby, leaf stoichiometry of herbs may be less affected by graphical and climatic factors. Vines have faster resource acquisition strategy than woody plants [95]; thus, their nutrient concentration may also be less sensitive to climatic change. However, the leaf N:P ratio was relatively stable and significant correlation with climatic and geographical variables across the different life forms (Figure 6c), which is inconsistent with the trends found in recent studies [4,25,96]. This inconsistency again suggested that study of biogeographic patterns of leaf nutrients at regional scales is increasingly important to accurately understand the relationship between vegetation and climate at the global scale.
5. Conclusions
The present study showed that average N, P concentration and N, P stoichiometric ratio of 345 plant samples from 268 species in Hainan Island were different from global scale and other regions, suggesting that plant stoichiometric pattern is unique in tropical regions. Leaf N concentration was negatively correlated with latitude and MAT, but was positively related to longitude and ALT; leaf P concentration was negatively associated with latitude and MAT, but was positively correlated with MAP; and leaf C:N, C:P, and N:P ratio was also related to some geographical and climatic factors. These results confirmed our hypothesis and suggest that geographical and climatic factors have great effect on plant stoichiometry in Hainan Island. In addition, the correlation between plant stoichiometry and geographical and climatic factors changed across life forms, indicating that plants of different life forms have different resource utilization efficiencies and environmental adaptation strategies. Our results contribute to the understanding of the spatial patterns of leaf stoichiometry in a wide variety of tropical plants and their response to global climate change, which may play a crucial role in guiding the nutrient management in tropical rainforest.
Author Contributions
Writing—original draft preparation, formal analysis, investigation, methodology, J.W. and Y.L.; writing—review and editing, G.W.; investigation, X.L., J.L. and H.W.; conceptualization, Z.C. and B.W. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the National Natural Science Foundation of China (No. 42167011), the Hainan Province Science and Technology Special Fund (No. ZDYF2021GXJS038), and Opening Project Fund of Key Laboratory of Rubber Biology and Genetic Resource Utilization, Ministry of Agriculture/State Key Laboratory Breeding Base of Cultivation and Physiology for Tropical Crops/Danzhou Investigation and Experiment Station of Tropical Crops, Ministry of Agriculture (RRI-KLOF202204).
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
Plant species status.
| Serial Number | Plant Name | Life Form | Evergreen/Deciduous Plant |
|---|---|---|---|
| 1 | Alangium chinense | Woody plant | Deciduous plant |
| 2 | Artocarpus hypargyreus | Woody plant | Evergreen |
| 3 | Abelmoschus esculentus | Herbs | / |
| 4 | Acacia confusa | Woody plant | Evergreen |
| 5 | Acalypha wikesiana | Woody plant | Evergreen |
| 6 | Acanthopanax senticosus | Woody plant | Evergreen |
| 7 | Acer buergerianum | Woody plant | Deciduous plant |
| 8 | Achyranthes bidentata | Herbs | / |
| 9 | Acmena acuminatissima | Woody plant | Evergreen |
| 10 | Acronychia pedunculata | Woody plant | Evergreen |
| 11 | Actinidia chinensis | Vine | Deciduous plant |
| 12 | Adenanthera pavonlna | Woody plant | Deciduous plant |
| 13 | Aeschynomene indica | Herbs | / |
| 14 | Aidia cochinchinensis | Woody plant | Evergreen |
| 15 | Alangium salviifolium | Woody plant | Deciduous plant |
| 16 | Albizia chinensis | Woody plant | Evergreen |
| 17 | Albizzia corniculata | Vine | Evergreen |
| 18 | Albizzia procera | Woody plant | Deciduous plant |
| 19 | Alchornea davidii | Woody plant | Deciduous plant |
| 20 | Alchornea trewioides | Woody plant | Evergreen |
| 21 | Aleurites moluccana | Woody plant | Evergreen |
| 22 | Allamanda cathartica | Woody plant | Evergreen |
| 23 | Alocasia macrorrhiza | Herbs | / |
| 24 | Alpinia japonica | Herbs | / |
| 25 | Alpinia zerumbet | Herbs | / |
| 26 | Alseodaphne rugosa | Woody plant | Evergreen |
| 27 | Alstonia scholaris | Woody plant | Evergreen |
| 28 | Annona glabra | Woody plant | Evergreen |
| 29 | Annona montana | Woody plant | Evergreen |
| 30 | Aphanamixis polystachya | Woody plant | Evergreen |
| 31 | Aporosa dioica | Woody plant | Evergreen |
| 32 | Aquilaria sinensis | Woody plant | Evergreen |
| 33 | Araucaria cunninghamii | Woody plant | Evergreen |
| 34 | Ardisia japonica | Woody plant | Evergreen |
| 35 | Areca catechu | Woody plant | Evergreen |
| 36 | Areca triandra | Woody plant | Evergreen |
| 37 | Arenga pinnata | Woody plant | Evergreen |
| 38 | Bambusa textilis | Herbs | / |
| 39 | Bidens pilosa | Herbs | / |
| 40 | Blastus cochinchinensis | Woody plant | Evergreen |
| 41 | Bombax malabaricum | Woody plant | Deciduous plant |
| 42 | Bowringia callicarpa | Woody plant | Evergreen |
| 43 | Brucea javanica | Woody plant | Evergreen |
| 44 | Buxus megistophylla | Woody plant | Evergreen |
| 45 | Byttneria aspere | Vine | Evergreen |
| 46 | Caesalpinia pulcherrima | Woody plant | Evergreen |
| 47 | Calliandra haematocephala | Woody plant | Deciduous plant |
| 48 | Callistemon rigidus | Woody plant | Evergreen |
| 49 | Camptotheca acuminata | Woody plant | Deciduous plant |
| 50 | Canarium pimela | Woody plant | Evergreen |
| 51 | Carica papaya | Herbs | / |
| 52 | Carmona microphylla | Woody plant | Evergreen |
| 53 | Carvota mitis | Woody plant | Evergreen |
| 54 | Caryota mitis | Woody plant | Evergreen |
| 55 | Caryota ochlandra | Woody plant | Evergreen |
| 56 | Cayratia japonica | Vine | Evergreen |
| 57 | Cecropia peltata | Woody plant | Evergreen |
| 58 | Ceiba pentandra | Woody plant | Deciduous plant |
| 59 | Ceiba speciosa | Woody plant | Deciduous plant |
| 60 | Celosia argentea | Herbs | / |
| 61 | Cerbera manghas | Woody plant | Evergreen |
| 62 | Chamaedorea erumpens | Woody plant | Evergreen |
| 63 | Choerospondias axillaris | Woody plant | Deciduous plant |
| 64 | Chromolaene odorata | Herbs | / |
| 65 | Chrysalidocarpus lutescens | Woody plant | Evergreen |
| 66 | Chukrasia tabularis | Woody plant | Evergreen |
| 67 | Cinnamomum bodinieri | Woody plant | Evergreen |
| 68 | Cinnamomum pedunculatum | Woody plant | Evergreen |
| 69 | Citrus maxima | Woody plant | Evergreen |
| 70 | Clerodendrum trichotomum | Woody plant | Evergreen |
| 71 | Cocos uncifera | Woody plant | Evergreen |
| 72 | Codiaeum variegatum | Woody plant | Evergreen |
| 73 | Cola acuminata | Woody plant | Evergreen |
| 74 | Conyza canadensis | Herbs | / |
| 75 | Cordyline fruticosa | Woody plant | Evergreen |
| 76 | Costus speciosus | Herbs | / |
| 77 | Crassocephalum crepidioides | Herbs | / |
| 78 | Cratoxylum cochin chinense | Woody plant | Deciduous plant |
| 79 | Croton laevigatus | Woody plant | Evergreen |
| 80 | Cudrania cochin chinensis | Woody plant | Evergreen |
| 81 | Curculigo orchioides | Herbs | / |
| 82 | Dalbergia hupeana | Woody plant | Evergreen |
| 83 | Delonix regia | Woody plant | Deciduous plant |
| 84 | Grona heterocarpos | Woody plant | Evergreen |
| 85 | Desmos chinensis | Woody plant | Evergreen |
| 86 | Dianella ensifolia | Herbs | / |
| 87 | Digitaria sanguinalis | Herbs | / |
| 88 | Dimocarpus longan | Woody plant | Evergreen |
| 89 | Dioscorea opposita | Vine | Evergreen |
| 90 | Diospyros ebenum | Woody plant | Evergreen |
| 91 | Dolichandrone stipulata | Woody plant | Evergreen |
| 92 | Dracaena angustifolia | Woody plant | Evergreen |
| 93 | Dracontomelon duperreanum | Woody plant | Evergreen |
| 94 | Duranta repens | Woody plant | Evergreen |
| 95 | Elaeagnus pungens | Woody plant | Evergreen |
| 96 | Elaeis guineensis | Woody plant | Evergreen |
| 97 | Elephantopus scaber | Herbs | / |
| 98 | Elephantopus tomentosus | Herbs | / |
| 99 | Eleusine indica | Herbs | / |
| 100 | Elsholtzia ciliata | Herbs | / |
| 101 | Engelhardtia roxburghiana | Woody plant | Evergreen |
| 102 | Erythrophleum fordii | Woody plant | Evergreen |
| 103 | Eugenia uniflora | Woody plant | Evergreen |
| 104 | Euphorbia humifusa | Herbs | / |
| 105 | Evodia glabrifolia | Woody plant | Evergreen |
| 106 | Evodia lepta | Woody plant | Evergreen |
| 107 | Fagraea ceilanica | Woody plant | Evergreen |
| 108 | Ficus altissima | Woody plant | Evergreen |
| 109 | Ficus auriculata | Woody plant | Evergreen |
| 110 | Ficus benjamina | Woody plant | Evergreen |
| 111 | Ficus fistulosa | Woody plant | Evergreen |
| 112 | Ficus hirta | Woody plant | Evergreen |
| 113 | Ficus hispida | Woody plant | Evergreen |
| 114 | Ficus microcarpa | Woody plant | Evergreen |
| 115 | Ficus subpisocarpa | Woody plant | Evergreen |
| 116 | Ficus tinctoria | Woody plant | Evergreen |
| 117 | Fissistigma oldhamii | Woody plant | Evergreen |
| 118 | Garcia nutans | Woody plant | Evergreen |
| 119 | Garcinia oblongifolia | Woody plant | Evergreen |
| 120 | Gardenia jasminoides | Woody plant | Evergreen |
| 121 | Gleditsia sinensis | Woody plant | Deciduous plant |
| 122 | Gleditsia vestita | Woody plant | Evergreen |
| 123 | Gmelina arborea | Woody plant | Evergreen |
| 124 | Gnetum parvifolium | Vine | Evergreen |
| 125 | Grevillea banksii | Woody plant | Evergreen |
| 126 | Gynura segetum | Herbs | / |
| 127 | Hamelia patens | Woody plant | Evergreen |
| 128 | Hedera nepalensis | Woody plant | Evergreen |
| 129 | Hedyotis auricularia | Herbs | / |
| 130 | Hedyotis hedyotidea | Vine | Evergreen |
| 131 | Heritiera angustata | Woody plant | Evergreen |
| 132 | Heritiera parvifolia | Woody plant | Evergreen |
| 133 | Hernandia sonora | Woody plant | Evergreen |
| 134 | Hevea brasiliensis | Woody plant | Deciduous plant |
| 135 | Hibiscus mutabilis | Woody plant | Deciduous plant |
| 136 | Hibiscus rosa-sinensis | Woody plant | Evergreen |
| 137 | Hibiscus schizopetalus | Woody plant | Evergreen |
| 138 | Holmskioldia sanguinea | Woody plant | Evergreen |
| 139 | Holarrhena antidysenterica | Woody plant | Evergreen |
| 140 | Homalium cochinchinense | Woody plant | Evergreen |
| 141 | Homalium hainanense | Woody plant | Evergreen |
| 142 | Hopea exalata | Woody plant | Evergreen |
| 143 | Hoya carnosa | Vine | Evergreen |
| 144 | Hymenaea courbaril | Woody plant | Evergreen |
| 145 | Ilex asprella | Woody plant | Deciduous plant |
| 146 | Ipomoea biflora | Herbs | / |
| 147 | Ixora chinensis | Woody plant | Evergreen |
| 148 | Jasminum lanceolarium | Woody plant | Evergreen |
| 149 | Juncellus serotinus | Herbs | / |
| 150 | Kigelia pinnata | Woody plant | Deciduous plant |
| 151 | Lantana camara | Herbs | / |
| 152 | Lasianthus chinensis | Woody plant | Evergreen |
| 153 | Lasianthus japonicus | Woody plant | Evergreen |
| 154 | Leptodermis parkeri | Woody plant | Evergreen |
| 155 | Ligustrum vicaryi | Woody plant | Deciduous plant |
| 156 | Litchi chinensis | Woody plant | Evergreen |
| 157 | Lithocarpus corneus | Woody plant | Evergreen |
| 158 | Litsea monopetala | Herbs | / |
| 159 | Litsea pungens | Woody plant | Deciduous plant |
| 160 | Lophatherum | Woody plant | Evergreen |
| 161 | Lucuma nervosa | Woody plant | Evergreen |
| 162 | Machilus salicina | Woody plant | Evergreen |
| 163 | Maesa japonica | Woody plant | Evergreen |
| 164 | Magnolia coco | Woody plant | Evergreen |
| 165 | Magnolia denudata | Woody plant | Deciduous plant |
| 166 | Magnolia liliflora | Woody plant | Evergreen |
| 167 | Mallotus apelta | Woody plant | Evergreen |
| 168 | Mallotus hookerianus | Woody plant | Evergreen |
| 169 | Malvastrum coromandelianum | Herbs | / |
| 170 | Manihot esculenta | Woody plant | Evergreen |
| 171 | Manilkara zapota | Woody plant | Evergreen |
| 172 | Melastoma candidum | Herbs | / |
| 173 | Melastoma sanguineum | Herbs | / |
| 174 | Mesua ferrea | Woody plant | Evergreen |
| 175 | Michelia odora | Woody plant | Evergreen |
| 176 | Mimosa pudica | Herbs | / |
| 177 | Mimosa sepiaria | Herbs | / |
| 178 | Mimusops elengi | Woody plant | Evergreen |
| 179 | Miscanthus sinensis | Herbs | / |
| 180 | Moghania macrophylla | Woody plant | Evergreen |
| 181 | Mucuna sempervirens | Vine | Evergreen |
| 182 | Muntingia calabura | Woody plant | Evergreen |
| 183 | Musa nana | Herbs | / |
| 184 | Nephelium lappceum | Woody plant | Evergreen |
| 185 | Pacrydium pierrei | Woody plant | Evergreen |
| 186 | Paederia scandens | Vine | Evergreen |
| 187 | Paeonia suffruticosa | Woody plant | Deciduous plant |
| 188 | Pandanus tectorius | Woody plant | Evergreen |
| 189 | Parakmeria lotungensis | Woody plant | Evergreen |
| 190 | Passiflora foetida | Vine | Evergreen |
| 191 | Pharbitis nil | Herbs | / |
| 192 | Photinia serrulata | Woody plant | Evergreen |
| 193 | Phragmites australias | Herbs | / |
| 194 | Phyllanthus emblica | Woody plant | Evergreen |
| 195 | Phyllanthus urinaria | Herbs | / |
| 196 | Pittosporum tobira | Woody plant | Evergreen |
| 197 | Platycladus orientalis | Woody plant | Evergreen |
| 198 | Plumeria rubra | Woody plant | Deciduous plant |
| 199 | Podocarpus imbricatus | Woody plant | Evergreen |
| 200 | Pollia japonica | Herbs | / |
| 201 | Polyalthia longifolia | Woody plant | Evergreen |
| 202 | Polyalthia rumphii | Woody plant | Evergreen |
| 203 | Polygala japonica | Herbs | / |
| 204 | Polygonatum odoratum | Herbs | / |
| 205 | Polygonatum sibiricum | Herbs | / |
| 206 | Pongamia pinnata | Woody plant | Evergreen |
| 207 | Portulaca grandiflora | Herbs | / |
| 208 | Pothos chinensis | Vine | Evergreen |
| 209 | Pouzolzia zeylanica | Herbs | / |
| 210 | Psychotria rubra | Woody plant | Evergreen |
| 211 | Pterocarpus marsupium | Woody plant | Evergreen |
| 212 | Pterolobium punctatum | Vine | Evergreen |
| 213 | Pterospermum heterophyllum | Woody plant | Evergreen |
| 214 | Ptychosperma macarthurii | Woody plant | Evergreen |
| 215 | Pueraria lobata | Vine | Evergreen |
| 216 | Quercus variabilis | Woody plant | Evergreen |
| 217 | Quisqualis indica | Woody plant | Evergreen |
| 218 | Rhaphidophora hongkongensis | Vine | Evergreen |
| 219 | Rhapis excelsa | Woody plant | Evergreen |
| 220 | Rhodomyrtus tomentosa | Woody plant | Evergreen |
| 221 | Rhopalostylis sapida | Woody plant | Evergreen |
| 222 | Richardia scabra | Herbs | / |
| 223 | Rourea microphylla | Woody plant | Evergreen |
| 224 | Rubus corchorifolius | Woody plant | Evergreen |
| 225 | Russelia equisetiformis | Woody plant | Evergreen |
| 226 | Schinus terebinthifolius | Woody plant | Evergreen |
| 227 | Sabal mauritiformis | Woody plant | Evergreen |
| 228 | Sanchezia speciosa | Woody plant | Evergreen |
| 229 | Sapium sebiferum | Woody plant | Deciduous plant |
| 230 | Sarcandra glabra | Herbs | / |
| 231 | Schefflera octophylla | Woody plant | Evergreen |
| 232 | Setaria viridis | Herbs | / |
| 233 | Sida acuta | Herbs | / |
| 234 | Sida rhombifolia | Woody plant | Evergreen |
| 235 | Sindora glabra | Woody plant | Evergreen |
| 236 | Sinomenium acutum | Vine | Evergreen |
| 237 | Sloanea hemsleyana | Woody plant | Evergreen |
| 238 | Smilax china | Vine | Evergreen |
| 239 | Spathodea campanulata | Woody plant | Deciduous plant |
| 240 | Spermacoce latifolia | Herbs | / |
| 241 | Spondias lakonensis | Woody plant | Evergreen |
| 242 | Styrax suberifolius | Woody plant | Evergreen |
| 243 | Swietenia macrophylla | Woody plant | Evergreen |
| 244 | Symplocos caudata | Woody plant | Evergreen |
| 245 | Symplocos congesta | Woody plant | Evergreen |
| 246 | Synedrellanodiflora | Herbs | / |
| 247 | Synsepalum dulcificum | Woody plant | Evergreen |
| 248 | Syzygium buxifolium | Woody plant | Evergreen |
| 249 | Syzyglum hancei | Woody plant | Evergreen |
| 250 | Tectona grandis | Woody plant | Evergreen |
| 251 | Terminalia arjuna | Woody plant | Evergreen |
| 252 | Terminalia catappa | Woody plant | Evergreen |
| 253 | Tetracera asiatica | Vine | Evergreen |
| 254 | Thunbergia erecta | Woody plant | Evergreen |
| 255 | Tithonia diversifolia | Herbs | / |
| 256 | Toddalia asiatica | Woody plant | Evergreen |
| 257 | Toona sinensis | Woody plant | Deciduous plant |
| 258 | Trachelospermum jasminoides | Vine | Evergreen |
| 259 | Triumfetta rhomboidea | Woody plant | Evergreen |
| 260 | Urena lobata | Herbs | / |
| 261 | Uvaria boniana | Woody plant | Evergreen |
| 262 | Veitchia merrillii | Woody plant | Evergreen |
| 263 | Viburnum odoratissimum | Woody plant | Evergreen |
| 264 | Vitex quinata | Woody plant | Evergreen |
| 265 | Wedelia chinensis | Herbs | / |
| 266 | Zanthoxylum avicennae | Woody plant | Deciduous plant |
| 267 | Zanthoxylum bungeanum | Woody plant | Deciduous plant |
| 268 | Zingiber zerumbet | Herbs | / |
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Figure 1.
Location of the study area. Danzhou, Tunchang, Changjiang, and Wuzhishan on Hainan Island were selected as the sampling points. There were two sampling points in Wuzhishan, resulting in a total of five sampling points.
Figure 1.
Location of the study area. Danzhou, Tunchang, Changjiang, and Wuzhishan on Hainan Island were selected as the sampling points. There were two sampling points in Wuzhishan, resulting in a total of five sampling points.
Figure 2.
Correlation of N and P concentrations in leaves. Red solid line represents the significant correlation between leaf stoichiometry and geographical factors (p < 0.05, p < 0.01).
Figure 2.
Correlation of N and P concentrations in leaves. Red solid line represents the significant correlation between leaf stoichiometry and geographical factors (p < 0.05, p < 0.01).
Figure 3.
Correlation between leaf stoichiometry and geographical factors. Both the red dotted and blue solid lines represent significant correlations between leaf stoichiometry and geographical factors (p < 0.05, p < 0.01). No line indicates the absence of a significant correlation between leaf stoichiometry and geographical factors. ALT: altitude.
Figure 3.
Correlation between leaf stoichiometry and geographical factors. Both the red dotted and blue solid lines represent significant correlations between leaf stoichiometry and geographical factors (p < 0.05, p < 0.01). No line indicates the absence of a significant correlation between leaf stoichiometry and geographical factors. ALT: altitude.
Figure 4.
Correlation between leaf stoichiometry and climatic factors. Both the red dotted and blue solid lines represent significant correlations between leaf stoichiometry and climatic factors (p < 0.05, p < 0.01). No line indicates the absence of a significant correlation between leaf stoichiometry and climatic factors. MAP and MAT represent the mean annual precipitation and temperature, respectively.
Figure 4.
Correlation between leaf stoichiometry and climatic factors. Both the red dotted and blue solid lines represent significant correlations between leaf stoichiometry and climatic factors (p < 0.05, p < 0.01). No line indicates the absence of a significant correlation between leaf stoichiometry and climatic factors. MAP and MAT represent the mean annual precipitation and temperature, respectively.
Figure 5.
(a) Variance of N and P concentrations and (b,c) their stoichiometric ratios among different life forms (woody plants, herbs, and vines). Different lowercase letters above the bar indicate significant differences among the life forms for the same element, concentration, or ratio. (a) Description of N and P concentrations in the leaves of the different plant life forms. (b,c) description of stoichiometric ratios among the different life forms.
Figure 5.
(a) Variance of N and P concentrations and (b,c) their stoichiometric ratios among different life forms (woody plants, herbs, and vines). Different lowercase letters above the bar indicate significant differences among the life forms for the same element, concentration, or ratio. (a) Description of N and P concentrations in the leaves of the different plant life forms. (b,c) description of stoichiometric ratios among the different life forms.
Figure 6.
Heat map of Pearson’s matrix of correlation coefficients between leaf stoichiometry and geographical and climatic factors for different life forms. (a–c) represent the correlations of woody plants, herbs, vines with geographical and climatic factors, respectively. * and ** represent significant correlations at p < 0.05 and p < 0.01, respectively. ALT, MAP, and MAT represent altitude, mean annual precipitation, and mean annual temperature, respectively.
Figure 6.
Heat map of Pearson’s matrix of correlation coefficients between leaf stoichiometry and geographical and climatic factors for different life forms. (a–c) represent the correlations of woody plants, herbs, vines with geographical and climatic factors, respectively. * and ** represent significant correlations at p < 0.05 and p < 0.01, respectively. ALT, MAP, and MAT represent altitude, mean annual precipitation, and mean annual temperature, respectively.
Table 1.
Overview of the study area.
| Study Area | Wuzhishan | Danzhou | Changjiang | Tunchang | |
|---|---|---|---|---|---|
| Latitude | 18°55′45.46″ N | 18°47′40.22″ N | 19°30′50.94″ N | 19°07′21.87″ N | 19°27′48.29″ N |
| Longitude | 109°28′7.83″ E | 109°38′54.94″ E | 109°29′58.70″ E | 109°04′45.63″ E | 110°05′52.77″ E |
| Average Altitude (m) | 260 | 505 | 137 | 660 | 135 |
| MAT (°C) | 22.80 | 22.80 | 23.70 | 24.33 | 23.13 |
| MAP (mm) | 2080.95 | 2080.95 | 1934.99 | 1563.12 | 2105.15 |
| Average Sunshine Time (h) | 2000 | 1900 | 2300 | 2000 | |
| Soil Types | Yellow soil, Latosol | Latosol | |||
| Climate Type | Tropical alpine climate | Tropical monsoon climate | |||
MAP and MAT represent the mean annual precipitation and mean annual temperature, respectively.
Table 2.
Sample statistics.
| Study Area | Wuzhishan | Danzhou | Changjiang | Tunchang | |
|---|---|---|---|---|---|
| Life form | Woody plants | 58 | 83 | 62 | 44 |
| Herbs | 37 | 0 | 8 | 28 | |
| Vines | 7 | 0 | 13 | 5 | |
| Evergreen sample | 47 | 77 | 72 | 40 | |
| Deciduous plant sample | 18 | 6 | 3 | 9 | |
| Sample size | 102 | 83 | 83 | 77 | |
Sample size refers to the total number of woody plants, herbs, and vines samples.
Table 3.
Statistics of N and P concentrations and stoichiometric ratios in leaves.
| Items | Mean | SD | Minimum | Maximum | CV (%) |
|---|---|---|---|---|---|
| N (mg g−1) | 3.80 | 0.20 | 0.16 | 16.39 | 5.26 |
| P (mg g−1) | 1.82 | 0.07 | 0.24 | 7.18 | 3.85 |
| C:N ratio (C:N) | 278.77 | 15.86 | 20.59 | 2865.25 | 5.69 |
| C:P ratio (C:P) | 390.69 | 15.15 | 47.47 | 1756.33 | 3.88 |
| N:P ratio (N:P) | 2.25 | 0.10 | 0.14 | 14.70 | 4.44 |
SD represents standard deviation, CV indicates coefficient of variation.
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Wang, J.; Liang, Y.; Wang, G.; Lin, X.; Liu, J.; Wang, H.; Chen, Z.; Wu, B. Leaf Nitrogen and Phosphorus Stoichiometry and Its Response to Geographical and Climatic Factors in a Tropical Region: Evidence from Hainan Island. Agronomy 2023, 13, 411. https://doi.org/10.3390/agronomy13020411
AMA Style
Wang J, Liang Y, Wang G, Lin X, Liu J, Wang H, Chen Z, Wu B. Leaf Nitrogen and Phosphorus Stoichiometry and Its Response to Geographical and Climatic Factors in a Tropical Region: Evidence from Hainan Island. Agronomy. 2023; 13(2):411. https://doi.org/10.3390/agronomy13020411
Chicago/Turabian StyleWang, Jingjing, Yongyi Liang, Guoan Wang, Xiaoyan Lin, Jiexi Liu, Hao Wang, Zixun Chen, and Bingsun Wu. 2023. "Leaf Nitrogen and Phosphorus Stoichiometry and Its Response to Geographical and Climatic Factors in a Tropical Region: Evidence from Hainan Island" Agronomy 13, no. 2: 411. https://doi.org/10.3390/agronomy13020411
APA StyleWang, J., Liang, Y., Wang, G., Lin, X., Liu, J., Wang, H., Chen, Z., & Wu, B. (2023). Leaf Nitrogen and Phosphorus Stoichiometry and Its Response to Geographical and Climatic Factors in a Tropical Region: Evidence from Hainan Island. Agronomy, 13(2), 411. https://doi.org/10.3390/agronomy13020411
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