In order to understand the relationship between forest vegetation and environmental
gradient, I selected several functional traits of woody species to measure, expecting that
the traits can reflect the environmental gradient. Because this study is a pioneer study to
understand how environmental filter act on traits in our lab project, I selected those traits
that might be influenced by elevation gradient. I introduce these traits in the following
paragraphs.
I measured 14 functional traits, including leaf area (LA), specific leaf area (SLA),
leaf dry-matter content (LDMC), leaf thickness (Lth), succulence, chlorophyll content
(Chlmass), leaf water repellency (Dropupper and Dropbelow), venation density (VD), wood
density (WD), stable isotope ratio of nitrogen and carbon of leaf (δ15N and δ13C) and
content of nitrogen and carbon per mass in leaf (Nmass and Cmass).
Specific leaf area (SLA), defined as leaf area divided by dry mass, is related to life
history strategy, nutrient content and maximum photosynthetic rate (Wright et al. 2004),
and usually correlates with plant growth. For example, SLA is positively related to leaf
nitrogen content (Reich et al. 1997) and negatively related to carbon content (Hoffmann
et al. 2005). Therefore, the trait might play important role in plant resource use strategies.
Leaf dry matter content (LDMC) is the ratio of leaf dry mass to fresh mass. This trait
provides information of how plant invest their nutrient. Plants investment more resource
to build the construction of leaf would have higher LDMC and more rigidity leaf. Higher
LDMC means that the leaf is relatively tough, resistant to physical hazards and
decomposes slowly (Bakker et al. 2011).
Leaf thickness (Lth) and chlorophyll content (Chlmass) have been found to be good
surrogates of photosynthetic rate (Enriquez et al. 1996; Muraoka & Koizumi 2005).
Leaves with higher chlorophyll content have increased photosynthetic capacity (Muraoka
& Koizumi 2005). In previous studies, researchers found that plants growing at high
elevation have less chlorophyll content (Asner & Martin 2016). Thick leaves have high
photosynthetic rate because they contain more chlorophyll per leaf area and have more
capacity to do photosynthesis (Niinemets 1999; Mendes & Marenco 2010). In the other
hand, the increase in leaf thickness reduces CO2 diffusion through the tissue and slows
the photosynthesis (Mediavilla et al. 2001). Another trait which also correlates with the
plant physiological response is leaf succulence. Succulence is a proxy of water storage
capacity, it would affect the water status of leaves hence influences the gas exchange of
photosynthesis (Zhang et al. 2015).
Leaf area (LA) and wood density (WD) are often correlated with plant’s growth.
Plants that have fast growth rate usually have lower wood density and larger leaf area
(Kunstler et al. 2016). I also observed that low elevation species commonly have large
leaves and high elevation species have smaller leaves. Considering that lowland is warmer
than high elevation, these two traits may represent the indirect effect of temperature on
growth rate. Some studies also reported that wood density is a good predictor of drought
tolerance of tropical tree species (Markesteijn et al. 2011), that is, species with dense
wood have better ability to tolerate drought.
Leaf water repellency can influences the water availability of the plants (Rosado &
Holder 2013). Some studies reported that leaf change water repellency in order to adapt
to shady conditions or to high amount of precipitation (Malhado et al. 2012; Meng et al.
2014). The air humidity in cloud forests is typically high. The droplet attached on the leaf
surface may influence the gas exchange through stomata. Leaf water repellency is also
considered as a factor influencing the water balance in the cloud forest and plays an
important role for hydrological processes (Holder 2007). Accordingly, leaf water
repellency may be a good trait indicating the suitability of the species to grow in the cloud
forest.
Leaf venation density (VD) is used as a taxonomic character in botany. But in recent
years, some studies revealed that leaf vein density may be influenced by various
environmental factors (Sack et al. 2012). Vein density plays an important role in
connecting hydraulics and photosynthesis and is also a proxy of the effect of climate and
environment (Uhl & Mosbrugger 1999; Brodribb et al. 2007). This makes leaf venation a
functional trait demonstrating how plants adapt to a different environment. Veins
belonging to different levels have different functions. The major veins are mainly
composed by sclerenchyma and provide leaf mechanical support, while minor veins can
improve phloem loading for transporting nutrients (Niklas 1999; Roth-Nebelsick et al.
2001; Sack et al. 2012). To overcome the disturbance from wind, plants might need more
mechanical support in the leaves. It has been found that VD is negatively correlated with
elevation gradient and also negatively related to water availability (Uhl & Mosbrugger
1999; Brodribb et al. 2007).
Chemical content in leaf is also a good indicator for plant’s lifespan. Carbon content
(Cmass) indicates how much photosynthates was invested by the plant. Nitrogen content
(Nmass) is positively correlated with the efficiency of photosynthesis and chlorophyll
content (Ripullone et al. 2003). The stables isotopes ratio of the carbon and nitrogen (δ13C
and δ15N) have been used to interpret plant’s physiological status. The ratio of leaf stable
carbon isotopes (δ13C) is used to identify photosynthetic pathways of plants (ie.C3, C4 and
CAM). δ13C of C3 plant is usually from -35‰ to -20‰. C4 plants are around -15‰ to -11
‰ (Dawson et al. 2002). δ13C is also used as a common proxy to estimate water use
efficiency (WUE) (Saurer et al. 2004). The ratio of leaf stable nitrogen isotopes (δ15N)
have been used to characterize local nitrogen cycling process (Bai et al. 2009). Leaf δ15N
is also used to trace the source of available N for the plants.
1.3 The objectives of the study
Most of traits studies present background about how functional traits influence the fitness
of plants under given environmental conditions (Lasky et al. 2013; Adler et al. 2014;
Reich 2014). In this study, I took both more and less commonly measured functional traits
from different species, which represent different dimensions of plant’s adaptation, and
focused on the relationship between functional traits and environmental gradient
(elevation). Additionally, I also focused on trait-trait relationship, which provides basic
information for describing how functional traits work on plants fitness. According to this
separation, it can interpret clearly how environmental gradients influence the community
composition through traits. Actually, trait-trait relationships may vary significantly by the
environmental difference (Wright et al. 2005). Thus, trait-trait relationships are important
background for trait-environment relationship.
According to the above mentioned, I asked the following questions: (1) What is the
interrelationship among traits, and which plant life-history strategy be defined by these
traits? (2) By which traits – leaf and wood is the environment filtering the species into the
vegetation community?
Materials and Methods
2.1 Study site
The study site is located in La-La Shan area in Northeast Taiwan (Figure 1), along
elevation gradient from Ba-Dao-Er Shan (low elevation limit) in Wulai (N 24°51'15.91",
E 121°31'47.86") to Ta-Man Shan (high elevation limit) in Fuxing (N 24°42'12.31", E
121°26'47.6"). Elevation range is between 850 m a.s.l to 2100 m a.s.l, study area is on the
margin of New Taipei City district and Taoyuan City district. Climate in this area is warm
and wet (Figure 2), with average annual temperature 16.1°C and average annual
Figure 1: Map of the study area.
precipitation 2070 mm. Mean monthly temperature for the coldest month (January) is
6.2 C and for the warmest month (July) is 24.8°C. This weather data were collected by
the closest weather station established by Central Weather Bureau (La-La Shan,
24°41'25.02" N, 121°24'14.1" E, elevation 1374 m a.s.l., data recorded from 2008 to
2017). According to geographical climatic regions distinguished by Su (1985), the study
area is on the boundary of Northeast and Northwest region. Types of geological substrates
are argillite, shale, slate, sandstone and phyllite (Central Geological Survey, MOEA). The
forest vegetation is represented by three types: high elevation Chamaecyparis montane Figure 2: Climatic diagram of La-La Shan station in La-La Shan.
mixed cloud forest, mid-elevation Quercus montane evergreen broad-leaved cloud forest
and low elevation Pyrenaria–Machilus subtropical winter monsoon forest (Li et al. 2013).
2.2 Sampling design
The sampling design has 18 400-m2 and
60 100-m2 plots, distributed into six
elevation zones－850, 1100, 1350, 1600,
1850 and 2100 m a.s.l. For 400-m2 plots,
each elevation zone according to the
topography and monsoon direction, plots
were assigned into three groups－leeward, ridge and windward side. Windward side plots
faces northeast while leeward plots southwest direction. Each plot is a square 20 m×20 m
of total area 400 m2. 100-m2 plots are squared, 10×10 meters (area of 100 m2). Each
elevation has 10 100-m2 plots, including three that are subplots of 400-m2 plots.
According to their topographical position, 100-m2 plots can be also classified into leeward,
ridge and windward. The schema of the sampling design is presented on Figure 3. Each
plot also has its own three-letter code, with the first letter for transect (L = La-La Shan),
the second letter for elevation zone (1 for highest and 6 for lowest), and the third letter
for aspect (L for leeward, R for ridge and W for windward). For example, L1W means Figure 3: Schema of sampling design.
the plot in the highest elevation zone (2100 m a.s.l.) facing the northeast direction
(windward).
2.3 Species abundance
In 400-m2 plots, I surveyed the species composition and measured DBH of each
individual (only individuals with DBH ≥ 1 cm were recorded). Species abundance is
calculated using importance value index. The equation for calculating IVI of each species
is given by
IVI = (relative abundance+ relative basal area)/2 (eq. 1)
Relative abundance is calculated as number of individuals tree of one species at the plot
divided by the number of all individual trees at the plot. To calculate relative basal area,
I calculated basal area of each species. First, I used DBH to calculate basal area, as
(DBH/2)2 of each individual. After that, the stem area of all individuals of the same
species was summed to get the basal area of that species. The basal area of each species
was divided by the sum of basal area of all individual trees in one plot to attain the relative
basal area of each species.
In the 100-m2 plots, I surveyed the species composition (only individuals taller than
two meters were recorded) without measuring the DBH for each individual. I had totally
61 100-m2 plots along the elevation gradient; all the elevation zones have 10 plots except
1600 m a.s.l. elevation zone has 11 plots. I chose the plots in which over 80 % of
individuals have measured trait values (one plot from the 2100 m elevation did not fit this
criteria, since it was dominated by coniferous species, and was removed from the
analysis). As a result, I used 60 100-m2 plots to do analysis.
2.4 Environmental factors
We record geographical coordinates and took several photographs for recording the
situation for each plot when we did the survey. Geographical coordinates were recorded
by GPS (GARMIN GPSMAP 64st, USA).
2.5 Trait sampling
Between December 2016 and September 2018, I collected and measured traits of 215
broad-leaf tree individuals. I also used data from other 250 individuals which were
collected and measured in October 2014 in a previous project (Zelený & Li, unpublished
data). Because at that time was the beginning of the project, our lab collected the samples
to see the pattern. This study take the data from that to make database more complete.
Altogether, the database contains traits from 465 individuals belonging to 119 broad-leaf
tree species.
Individuals of dominant species taller than two meters were selected for the sampling.
If one species was very dominant at one plot, I chose one or two biggest trees for sampling.
Before collecting, I measured each tree’s diameter at breast height (DBH). For each
individual, I collected several twigs with at least six leaves for measuring leaf traits. I
collected leaves, which were growing on the top of the canopy and were exposed to full
sun. In order to collect leaves from the upper part of the canopy, I used telescopic knife
(used by farmers to collect betel nuts, longest length around 14 meters) to cut the branch
with several leaves, then I picked up the small twig with entire leaves. After collecting at
least six entire leaves, I put them into plastic zipper bags with moistened paper or toilet
tissue. Before transporting the samples back to laboratory, I put plastic bags in portable
cooler with ice if possible. After arriving at the laboratory, I put plastic bags in a
refrigerator of 4°C.
Wood samples were collected in two ways. For individuals with DBH larger than 10
centimeters, I used increment borer (SUUNTO Increment Borers, Finland) with 5.15mm
diameter and followed the method described by Chave (2006). Before taking the sample
core, I used knife to remove mosses and outer bark of the tree at around the breast height.
Then I sticked the borer inside the cleaned spot on the trunk, and keep turning the handle
of borer until it got into the stem. When borer reached half of the stem, I turned the handle
a little bit back to break the core inside the borer. I used the extractor part of the borer to
get the entire wood core out. I put the core into plastic straw, cut strew into appropriate
length and seal both ends of the straw with parafilm M. I wrote the individual code on the
straw and put it into plastic zipper bag. For the individuals with DBH less than 10
centimeters, I directly took their branches with diameter larger than 1 centimeter and
length around 15 centimeters. Arriving at laboratory, I put all wood samples in a
refrigerator of 4°C and prepared them for measuring.
2.6 Trait measurement
I measured 14 functional traits, including leaf area (LA), specific leaf area (SLA), leaf
dry-matter content (LDMC), leaf thickness (Lth), succulence, chlorophyll content (Chl),
leaf water repellency (Dropupper and Dropbelow), venation density (VD), wood density
(WD), stable isotope ratio of nitrogen and carbon of leaf (δ15N and δ13C) and content of
nitrogen and carbon per mass in leaf (Nmass and Cmass). If not stated otherwise, the
measurement methods followed the handbookof Pérez-Harguindeguy et al. (2013).
For the measurement of functional traits, branches with leaves enclosed in plastic
bags were taken out from the refrigerator. Six entire leaves, from the branch sampled from
the same individual tree without damaged by the insect or covered by mosses were cut
and their petioles removed. These leaves were separated into two groups, each group has
three leaves. I then put the leaf into two transparent folders as soon as possible to avoid
the leaves losing water. After I finished collecting three to five individuals, I put the
remaining samples and one other folder which contains the other three leaves back to the
refrigerator. Samples in a prepared folder were waiting for the follow-up measurements.
For each measurement I used three leaves from each individual.
2.6.1 Leaf morphology measurements
Leaf fresh weight was measured by an electric balance (OHAUS Adventurer AR2140,
USA) with precision of 0.0001 g. Leaf thickness (Lth) was measured by digital display
thickness gauge (DML digital thickness gauge, UK) with precision of 0.001 mm. Primary,
secondary or obvious tertiary veins were avoided during the measurement. Leaf thickness
at upper right, upper left, lower right and lower left of the lamina were measured and
averaged. Leaf area (LA) was estimated by a scanner (Perfection V370 Photo, EPSON).
I put three leaves on the screen of scanner, upper lamina facing down, avoiding individual
leaves to overlap. Petioles of the leaves were positioned to the same direction. A ruler 5
centimeters long was placed on the corner of the screen as a scale. The scan resolution
was set to 300 dpi. After the image was scanned, leaf area was estimated by ImageJ
program (Fiji), which is a freeware software. After leaf scanning, each lamina was put
into an envelope folded from newspapers. All leaf samples in envelopes were dried in the
oven at 70°C for three or more days, to make sure the leaf was dry. I used again the
four-digit scales weight to measure the dry weight (LDW) of the leaf.
Specific leaf area (SLA) was calculated as one-side leaf area divided by leaf dry
weight (LA/LDW). Leaf dry-matter content (LDMC) was calculated from leaf dry weight
divided by the fresh weight (LDW/LFW). Leaf succulence was calculated from leaf dry
weigh, leaf fresh weight and leaf area (Mantovani 1999). The equation is following:
Succulence = (Leaf fresh weight- Leaf dry weight) *1000 / Leaf area (eq. 2)
2.6.2 Chlorophyll content (Chlmass)
A chlorophyll meter (SPAD-502, KONICA MINOLTA, Japan) was used for the
measurement of leaf chlorophyll content. I divided lamina into two parts, left and right,
and for each part I took the measurement at three random points. The average of these six
measurements represents the chlorophyll content for each leaf. In the previous project,
the chlorophyll content was measured by a different instrument (CCM-200, APOGEE,
USA). Because I wanted to use values measured both by SPAD and CCM in the same
analysis, I calibrate values measured by these two instruments, I did a small test to convert
the value of CCM-200 to SPAD-502 (Appendix 1). According to the result, I used the
regression line as calibration equation. The equation of calibration is following:
SPAD = -12.85+log(CCM)х18.26 (eq. 3)
I also transfer the chlorophyll content measured by SPED to chlorophyll content per
mass (Chlmass). The equation is Chlmass= ((117.1*SPAD)/(148.84-SPAD))*SLA (Coste et
al. 2010).
2.6.3 Leaf water repellency (Dropupper and Dropbelow)
Remaining leaves which were not used for other trait measurement were used for
measuring leaf water repellency and venation density (VD). For leaf water repellency
measurement, leaves were placed on flat custom-made platform (Figure 4) consisting of
one box (17.5 cm × 10 cm × 8 cm), one clipboard (10.6 cm × 18.5 cm), two rulers (around
12 cm) and four binder clips (two 32 mm and two 19 mm). I bounded the clipboard on
the box, using two bigger binder clips, and fixed the ruler on the clipboard from beside
and other two clip at the front side of the platform. Distance between two rulers was
around 0.7 cm. The following measurements were done for both adaxial (upper) and
abaxial (lower) surface of each leaf sample. Three leaves from each individual were
measured. The leaf was mounted horizontally between clipboard and rulers by binder
clips. One 5-μl droplet of distilled water was placed on the surface of lamina by a
micropipette (HIRSCHMANN labopette 2–20 μl single channel, Germany). Because
Figure 4: Platform for measuring droplet. (a) Front view (b) Top view
(a) (b)
droplet will spread and influence the angle of droplet, picture were taken as soon as
possible when water droplet was dropped on the leaf surface. Photos were taken by a
camera (NIKON D5500, Japan) equipped with lens (TAMRON SP AF 17-50 mm F2.8
XR Di II VC, Japan). Two or more photos for one treatment were taken. ImageJ (ImageJ
1.51u) with DropSnake plugin (Stalder et al. 2006) was used to analyze the photos. The contact angle (θ) was calculated following Aryal & Neuner (2010). Leaf surface was set
as the baseline, and the contact angle (θ) between a line at a tangent of the droplet running
through the point of contact between
the droplet and the leaf surface and
baseline was measured (Figure 5).
According to Crisp (1963), the leaves
with droplet angle higher than 110°
are considered as water repellent, and leaves with lower angle as not repellent.
2.6.4 Venation density (VD)
Leaves were cut into sections of 0.25–1 cm2, with the section size depending on the size
of the original lamina. This section is located at the middle of the leaf and avoids primary
and secondary veins. Three sections from one individual (one section per leaf, three leaves
per individual) were measured. In order to make vein more visible, I used clearing method Figure 5: Determination of the contact angle (θ). Figure is modified from Fig.2 of Aryal
& Neuner (2010).
to make the section transparent. Sections from the same individual were put into a 20 ml
of Vials Scintillation Glass, containing 5% NaOH–H2O. The sections were soaked for
24–72 hours. Soaking time depends on the species. Leaves of some species need longer
time to become transparent. The transparent leaf sections were rinsed the leaf in distilled
water for several times. 1% safranin O in distilled water was transferred inside the Vials
staining the sections for 15 minutes. After staining, sections were rinsed again with
distilled water until no visible red color in the rinsed water. The sections were temporarily
mounted by water on the slide. I then used microscope (OLYMPUS CX31, Japan) to
observe the sections and used a CCD camera (MICROTECH HDC200, USA) to take
pictures. Several pictures were taken to complete one section. When all photographs were
finished, I used Image Composite Editor (2015 Microsoft Corporation, Version 2.0.3.0)
to assemble all pictures together. After these procedures, image for the analysis of
venation density was completed. The transect method applied by Blonder & Enquist
(2014) was then used to estimate the venation density. I randomly draw line segments on
vein image to get the parameter d (in equation 4), then counted the number of veins the
line crossed. Parameter d is calculated as the total length of a line (70 mm) divided by the
number of veins the line crossed. I calculated the vein density using the following
equation:
VD = 0.629 × (1/d) + 1.073 (eq. 4)
2.6.5 Wood density (WD)
Two approaches were used to estimate wood density. For wood core collected by the borer,
Two approaches were used to estimate wood density. For wood core collected by the borer,