Leaf Nitrogen and Phosphorus Stoichiometry and Its Response to Geographical and Climatic Factors in a Tropical Region: Evidence from Hainan Island

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.

Full text

Agronomy 2023, 13, 411. https://doi.org/10.3390/agronomy13020411 www.mdpi.com/journal/agronomy
Article
Leaf Nitrogen and Phosphorus Stoichiometry and Its Response
to Geographical and Climatic Factors in a Tropical Region:
Evidence from Hainan Island
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
* Correspondence: chenzixun135@163.com (Z.C.); wubingsu11@163.com (B.W.)
† These authors contributed equally to this work.
Abstract: Leaf stoichiometry effectively indicates the response and adaptation of plants to environ-
mental 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 re-
sults 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.
Keywords: leaf stoichiometry; climate; geography; life form; Hainan Island
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, af-
fect plant growth and adaptation to terrestrial habitats and are closely related to global
Citation: 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
Academic Editors: Xiaodong Song,
Long Guo, Peng Fu and Shunhua
Yang
Received: 4 January 2023
Revised: 20 January 2023
Accepted: 25 January 2023
Published: 30 January 2023
Copyright: © 2023 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/).

Agronomy 2023, 13, 411 2 of 20
biochemical cycles [3]. N and P are closely related to plant photosynthesis, genetic mate-
rial composition, energy storage, and are the most important limiting nutrients in terres-
trial ecosystems [4–6]. In particular, the stoichiometry of N and P is closely related to plant
ecological strategies [7–11]. 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 en-
ergy cycles, both regionally and globally, thereby affecting vegetation activity and ecosys-
tem function [11,14–17]. For instance, warming can affect the rate of alter litter decompo-
sition and organic matter mineralization via changes in the soil’s physicochemical prop-
erties 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
[18–21]. 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 tem-
perature, 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 concentra-
tions 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 temper-
ature [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 addi-
tional 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 re-
duced 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 [11,29–35]. However, leaf stoichiome-
try 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 Hai-
nan 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 ge-
ographical and climatic factors in Hainan Island with high temperature and high

Agronomy 2023, 13, 411 3 of 20
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 relation-
ship between leaf N, P concentration, C:N, C:P, and N:P ratios; and climatic and geograph-
ical 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 geograph-
ically 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 Dan-
zhou, Changjiang, and Tunchang is tropical monsoon climate. Specific information re-
garding the study area is presented in Table 1.
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, respec-
tively.

Agronomy 2023, 13, 411 4 of 20
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, re-
sulting in a total of five sampling points.
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 col-
lected 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 Tun-
chang. 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 deter-
mined after sample digestion in H2SO4-H2O2, using a flow analyzer (Proxima1022/1/1, Al-
liance, France).
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.
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

Agronomy 2023, 13, 411 5 of 20
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 water use efficiency of different functional
plants in tropical and tropical regions: 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 respec-
tively, ranging from 0.16 to 16.39 mg g−1 and 0.24 to 7.18 mg g−1, respectively. The coeffi-
cient 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.
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.
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).
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

Agronomy 2023, 13, 411 6 of 20
3d,e). The C:N and N:P ratios did not change with longitude (Figure 3e,f). The leaf N con-
centration 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 in-
creasing 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 increas-
ing 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).
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 fac-
tors (p < 0.05, p < 0.01). No line indicates the absence of a significant correlation between leaf stoichi-
ometry and geographical factors. ALT: altitude.

Agronomy 2023, 13, 411 7 of 20
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, respec-
tively.
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, fol-
lowed 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).
Figure 5. (a) Variance of N and P concentrations and (b) and (c) their stoichiometric ratios among
different life forms (woody plants, herbs, and vines). Different lowercase letters above the bar indi-
cate 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) and (c) de-
scription of stoichiometric ratios among the different life forms.

Agronomy 2023, 13, 411 8 of 20
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 corre-
lated 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 lon-
gitude (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 concentra-
tions 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 alti-
tude, 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).
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 signifi-
cant correlations at p < 0.05 and p < 0.01, respectively. ALT, MAP, and MAT represent altitude, mean
annual precipitation, and mean annual temperature, respectively.
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 sig-
nificantly 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 cor-
related with MAT. The stoichiometric ratios of the other life forms were not significantly
correlated with MAT (Figure 6, p < 0.05).

Agronomy 2023, 13, 411 9 of 20
4. Discussion
4.1. Patterns of Leaf Stoichiometry in Hainan Island
Leaf stoichiometry is used as an important indicator to study plant nutrient limita-
tion, 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
(Tables 2 and 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 nitro-
gen, 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, geo-
chemical and biological processes are expected to occur at faster rates, resulting in intense
soil weathering [48–50]. 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, en-
hanced 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 av-
erage 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 concen-
tration 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 concen-
trations in short-lived and fast-growing species (e.g., annual herbaceous plants) are al-
ways 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 sug-
gested 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 re-
striction 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 sug-
gests that there may be a synergistic relationship between the N and P absorption effi-
ciency of plants on Hainan Island [65].

Agronomy 2023, 13, 411 10 of 20
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 (Figures 3 and 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 relation-
ship between leaf N, P concentrations and latitude [4,25,41]. However, a negative correla-
tion has been found between leaf N, P concentrations and latitude in Hainan Island (Fig-
ure 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 geo-
graphical patterns and even decreased with latitude [3,67,68]. C:N and C:P ratios are im-
portant 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 3a,b), implying that nu-
trient 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 ob-
served (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 precip-
itation may enhance the nutrient uptake capacity of plants [74–76], 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 be-
tween 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 mod-
ulated 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 concen-
trations 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 de-
creases from the southeast coast to the northwest inland region. Therefore, the longitudi-
nal 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 effi-
ciency 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

Agronomy 2023, 13, 411 11 of 20
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 [84–87]; thereby, leaf stoichiometry changes with altitude
[88–94]. Temperature decreases monotonically with increasing altitude, and leaf N con-
centration 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 correla-
tion 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. There-
fore, leaf element concentrations and their correlation with geographical and climatic fac-
tors 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 geo-
graphical 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 geo-
graphical 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 con-
firmed our hypothesis and suggest that geographical and climatic factors have great effect
on plant stoichiometry in Hainan Island. In addition, the correlation between plant stoi-
chiometry and geographical and climatic factors changed across life forms, indicating that
plants of different life forms have different resource utilization efficiencies and environ-
mental 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, methodol-
ogy, J.W. and Y.L.; writing—review and editing, G.W.; investigation, X.L., J.L. and H.W.; conceptual-
ization, 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, Min-
istry 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.

Agronomy 2023, 13, 411 12 of 20
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

Agronomy 2023, 13, 411 13 of 20
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

Agronomy 2023, 13, 411 14 of 20
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

Agronomy 2023, 13, 411 15 of 20
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

Agronomy 2023, 13, 411 16 of 20
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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