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\begin{document}

\title{Hydraulic vulnerability segmentation between branches and roots
increases with environmental aridity}



\author[1]{Qing Ye}%
\author[1]{Weize Tang}%
\author[1]{Hui Liu}%
\author[1]{Xingyun Liang}%
\author[1]{Pengcheng He}%
\author[1]{Han Zhao}%
\author[2]{Xiaorong Liu}%
\affil[1]{South China Botanical Garden}%
\affil[2]{Sichuan University of Arts and Science}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
The hydraulic vulnerability segmentation hypothesis proposes that plant
branches are more resistant to cavitation than roots, namely, the
difference in vulnerability to cavitation between branches and roots is
positive ( \emph{P} \textsubscript{50 root - branch} \textgreater{} 0).
However, it is not clear how this phenomenon can vary along
environmental aridity gradients. We compiled the above hypothesis with
105 woody species from four biomes with increasing aridity, by compiling
functional traits related to hydraulic properties and anatomical
structures of branches and roots. We investigated the relationships
between \emph{P} \textsubscript{50 root - branch} and several
environmental factors that are associated with aridity. We found a
positive \emph{P} \textsubscript{50 root - branch} across species, which
supported the hydraulic vulnerability segmentation hypothesis, and
\emph{P} \textsubscript{50 root - branch} increased significantly with
environmental aridity. Branch xylem hydraulic conductivity changed from
``more efficient'' (e.g., wider conduit, higher hydraulic conductivity)
to ``safer'' (e.g., narrower conduit, more negative \emph{P}
\textsubscript{50}) in response to increased aridity, while root xylem
hydraulic conductivity remained unchanged across aridity gradients. Our
results demonstrated that hydraulic vulnerability segmentation is more
pronounced for species from arid regions. Changes in branch traits may
be responsible for hydraulic vulnerability segmentation between branches
and roots.%
\end{abstract}%



\sloppy


\textbf{Hydraulic vulnerability segmentation between branches and roots
increases with environmental aridity}

Weize Tang\textsuperscript{1,3}, Hui Liu\textsuperscript{1}, Xingyun
Liang\textsuperscript{1}, Pengcheng He\textsuperscript{1}, Han
Zhao\textsuperscript{1}, Xiaorong Liu\textsuperscript{4}, Qing
Ye\textsuperscript{1,2,*}

\textsuperscript{1}Key Laboratory of Vegetation Restoration and
Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory
of Applied Botany, South China Botanical Garden, Chinese Academy of
Sciences, Guangzhou 510650, China;

\textsuperscript{2}College of Life Sciences, Gannan Normal University,
Ganzhou, 341000, China; \textsuperscript{3}University of Chinese Academy
of Sciences, Beijing, 100049 China;

\textsuperscript{4}Sichuan University of Arts and Science, Tashi Road
519, Dazhou 635000, China

* Corresponding author: Qing Ye

Address: No. 723, Xingke Road, Tianhe District, Guangzhou 510650, China

E-mail: qye@scbg.ac.cn

\textbf{Abstract}

The hydraulic vulnerability segmentation hypothesis proposes that plant
branches are more resistant to cavitation than roots, namely, the
difference in vulnerability to cavitation between branches and roots is
positive (\emph{P} \textsubscript{50 root - branch} \textgreater{} 0).
However, it is not clear how this phenomenon can vary along
environmental aridity gradients.

We compiled the above hypothesis with 105 woody species from four biomes
with increasing aridity, by compiling functional traits related to
hydraulic properties and anatomical structures of branches and roots. We
investigated the relationships between \emph{P} \textsubscript{50 root -
branch} and several environmental factors that are associated with
aridity.

We found a positive \emph{P} \textsubscript{50 root - branch} across
species, which supported the hydraulic vulnerability segmentation
hypothesis, and \emph{P} \textsubscript{50 root - branch} increased
significantly with environmental aridity. Branch xylem hydraulic
conductivity changed from ``more efficient'' (e.g., wider conduit,
higher hydraulic conductivity) to ``safer'' (e.g., narrower conduit,
more negative \emph{P} \textsubscript{50}) in response to increased
aridity, while root xylem hydraulic conductivity remained unchanged
across aridity gradients.

Our results demonstrated that hydraulic vulnerability segmentation is
more pronounced for species from arid regions. Changes in branch traits
may be responsible for hydraulic vulnerability segmentation between
branches and roots.

\textbf{Key-words:} cavitation, environment variables, hydraulic
conductivity, vulnerability segmentation, xylem anatomy

\textbf{Introduction}

Climate change is currently escalating so rapidly that many woody plants
may not be able to adapt the extreme drought caused by the gradual
global warming (Brodribb \emph{et al} ., 2020). A number of previous
studies documented an increased risk of forest mortality due to extreme
drought events (Klein \& Henrik, 2018; Hajek \emph{et al} ., 2022).
Continuous water transport supply from roots to stems and leaves through
xylem can be interrupted by cavitation and collapse of conduits during
drought (Tyree \& Sperry, 1989), which is tightly linked to
drought-induced mortality in plants (Choat \emph{et al} ., 2018). Roots
represent the direct interface of a plant to soil water, which is a
critical component of plant water transport pathway, and hydraulic
failure due to cavitation in the root system may directly lead to plant
mortality (Peters \emph{et al} ., 2020). Branches transport the water
absorbed by roots to leaves for continuation of photosynthesis and
transpiration. Investigating the coordination and dissonance of plant
adaptive traits between aboveground parts (i.e., branches) and
underground parts (i.e., roots) is particularly important for better
understanding plant survival strategies to climate change
(Aleixo\emph{et al} ., 2019).

The hydraulic vulnerability segmentation hypothesis is a plant adaptive
strategy to drought, which suggests that the terminal organs (e.g.,
leaves and roots) are first to be cavitated, thereby ensuring the
hydraulic safety of more expensive organs like trunk and branches (Tyree
\& Ewers, 1991). Hydraulic vulnerability of xylem can be quantified by
xylem pressure inducing 50\% loss of hydraulic conductivity (\emph{P}
\textsubscript{50}), which reflected plant resistance to drought
(Volaire, 2018). Previous studies have examined\emph{P}
\textsubscript{50} between roots and branches of woody species, and a
general finding is that roots are more vulnerable to drought-induced
cavitation than branches (i.e.,\emph{P} \textsubscript{50 root - branch}
\textgreater{} 0), and extensive cavitation in roots limited water
supply in plant hydraulic pathway during drought (Alder \emph{et al} .,
1996; Johnson \emph{et al} ., 2018). Investigating the vulnerability
segmentation of plants can help to judge whether xylem cavitation is
systemic, and whether it would cause a catastrophic and lethal failure
of the whole plant water transport system (Schenk \emph{et al} ., 2008;
Wu \emph{et al} ., 2020). Interestingly, some recent studies employing
optical methods found that roots have similar (e.g., \emph{Cedrus
deodara} and \emph{Eucalyptus saligna} ) (Peters \emph{et al} ., 2020)
or even stronger cavitation resistance than branches (e.g., \emph{Olea
europaea} ) (Rodriguez-Dominguez \emph{et al} ., 2018). Explanations for
these findings include that plant survival during drought would be best
served by adaptive xylem integrity where root and branch xylem exhibited
similar vulnerabilities, leading to shorter recovery time to roots
cavitation and greater investment in new root tissues after being
relieved from drought stress (Lamarque \emph{et al} ., 2018). However,
these studies focused on a few species from particular habitats, thus
comparisons of hydraulic traits between branches and roots across
species covering broad geographical regions are needed, to better
understand how resistance to drought is coordinated at the whole plant
level.

It has been shown that \emph{P} \textsubscript{50} varies widely among
plant species and is primarily determined by the differences in the
xylem structure (Lens \emph{et al} ., 2022). The more drought-tolerant
the plant, the more resistant the xylem to cavitation under negative
pressure, and the less likely the xylem conduit walls to implosion under
increased negative pressure (Zhao \emph{et al} ., 2020). Previous
studies reported that conduit wall thickness to span ratio (\emph{t}
\textsuperscript{2}/\emph{b} \textsuperscript{2}) is proportional to
\emph{P} \textsubscript{50} as required to avoid wall collapse (Cochard
\emph{et al} ., 2008; Rodriguez-Dominguez \emph{et al} ., 2018). In
addition, the pit area hypothesis (also called `rare pit' hypothesis)
proposed that the larger conduits tend to have a greater pitted wall
area, which is more vulnerable to cavitation (Hacke \emph{et al} .,
2017, Mrad \emph{et al} ., 2018). Therefore, whether roots have
anatomical properties that are more vulnerable to cavitation than
branches, is the structural basis for exploring the hydraulic
vulnerability segmentation between these two organs.

Environmental factors are important driving forces for the variability
of plant hydraulic traits. Trueba \emph{et al} ., (2017) reported that
branch \emph{P} \textsubscript{50} of rainforest species was
significantly correlated with elevation and mean annual temperature. In
addition, plants from arid regions were found to have narrow vessels or
tracheids (Gao \emph{et al} ., 2021). Meanwhile, vulnerability
segmentation between plant organs may shift with environmental factors.
For instance, Zhu \emph{et al} ., (2016) observed that vulnerability
segmentation between branches and leaves became less positive and even
negative with the increase of environmental aridity. However, it is lack
of studies to explore how environmental factors drive the potential
vulnerability segmentation between roots and branches, this question
deserves a more detailed analysis. Because a better understanding of the
linkages between \emph{P} \textsubscript{50} and environmental factors
in different organs could help to synthesize plant adaptive strategies,
and more precisely predict their responses to global climate change
(Li\emph{et al} ., 2015).

Here, we compiled a global dataset of 105 woody species (10 species were
measured in the present study) from 59 study sites worldwide. We
assembled \emph{P} \textsubscript{50} and \emph{K}
\textsubscript{S}(specific xylem conductivity per unit of
cross-sectional sapwood area) values measured simultaneously in the
xylem of branches and roots. In addition, we collected hydraulically
weighted mean diameter (\emph{D} \textsubscript{h}) and\emph{t}
\textsuperscript{2}/\emph{b} \textsuperscript{2} values to investigate
whether anatomical structures can explain the differences in xylem
vulnerability between roots and branches.

We had two hypotheses:

Roots of woody species would have more vulnerable xylem to
drought-induced cavitation than branches i.e., the difference in water
potential at 50\% loss of hydraulic conductance between branches and
roots is positive (\emph{P} \textsubscript{50 root -
branch}\textgreater{} 0). Hence, roots can sacrifice a small part of the
investment to protect plant hydraulic pathway from drought-induced
dysfunction in main organs (e.g., main root, trunk and branch).

The degree of vulnerability segmentation between branches and roots
decreases with the increasing water availability across different
habitats. Plants in arid habitats would have a higher degree of
segmentation (\emph{P} \textsubscript{50 root - branch} is more
positive) than those in humid areas. Like the segmentation between
branches and leaves, plants are likely to adopt a hydraulic strategy by
sacrificing terminal roots to prevent branches from hydraulic failure,
as drought increases.

\textbf{Materials and Methods}

\textbf{2.1 Study site and plant materials}

Field sampling was carried out in a species-rich subtropical old-growth
evergreen forest located in Dinghushan Forest Ecosystem Research
Station, Central Guangdong Province, South China (23\selectlanguage{ngerman}°09\selectlanguage{english}'21\selectlanguage{english}" N to
23deg11\selectlanguage{english}'30\selectlanguage{english}" N, 112deg30\selectlanguage{english}'39\selectlanguage{english}" E to 112deg33\selectlanguage{english}'41\selectlanguage{english}" E). The mean annual
precipitation is 1900 mm, nearly 80\% falls in the wet season from April
to September, and the mean annual temperature is 22.3 \selectlanguage{english} (Zhou \emph{et
al} ., 2011). The most dominant plant families in this study site
include\emph{Fagaceae} , \emph{Lauraceae} and \emph{Myrtaceae} (Li
\emph{et al} ., 2015), and the most dominant tree species are
\emph{Castanopsischinensis} , \emph{Schima superba} , \emph{Cryptocarya
chinensis} ,\emph{Machilus chinensis} and \emph{Syzygium levinei} (Zhu
\emph{et al} ., 2013).

Ten evergreen woody species were selected from this subtropical forest.
Characteristics of all the selected species, including diameter at
breast height (DBH) (range: 14.5-46.5 cm), were reported in supporting
information (Table S2). Three mature individuals per species
(total\emph{n} = 30) were selected. Branch and coarse root samples were
collected in September, 2021. At least three branch and root segments
were measured per individual. To collect branches, the top of the canopy
samples approximately 80 cm in length and of 6 mm (range: 4.1-9.2 mm) in
diameter were cut. Roots (soil depth: 5-25 cm) were excavated, and
traced from their parent tree to identify the species and then clipped
(approximately 80 to 90 cm in length and 4 to 9 mm in diameter). Branch
and root samples were immediately placed in black plastic bags with wet
paper towels, put into a cooler, transported back to the laboratory
within 60 minutes. Prior to hydraulic trait quantifications, for open
vessels were tested following the method described in Ewers \emph{et al}
., (1989). Only segments with no open vessels were used for subsequent
measurements.

We also collected data of root and branch \emph{P} \textsubscript{50}
and\emph{K} \textsubscript{S} along with the measurements of anatomical
traits, including vessel wall reinforcement (\emph{t}
\textsuperscript{2}/\emph{b} \textsuperscript{2}) and hydraulically
weighted diameters (\emph{D} \textsubscript{h}) (Table S1). The data
were derived from previous literature published between 1994 and 2021
using Web of Science (http://isiknowledge.com), Google Scholar
(https://scholar.google.com), and China National Knowledge
Infrastructure (http://www.cnki.net). The keywords used to search the
literature included ``root/branch hydraulic vulnerability'', ``xylem
cavitation'', ``xylem embolism'', and ``branch/stem hydraulic traits''.
Based on information in source publications and online floras (e.g.,
http://frps.eflora.cn), sites were assigned to biomes: tropical seasonal
forest (TRS), temperate seasonal forest (TMS), woodland and shrubland
(WDS), and desert (DES). To avoid systematic error, we selected data
that met the following criteria: (a) hydraulic and anatomical trait
measurements of sample segments came from the same plant, (b) experiment
was conducted in natural habitats, and (c) measurements were carried out
with were mature plants, not the saplings and seedlings. We took the
average value of the same species from the same site for data analyses.

\textbf{2.2 Xylem hydraulic traits}

Xylem cavitation resistance of roots and branches was constructed using
the air injection method (Sperry \& Saliendra, 1994). Previous studies
have shown that this method can obtain reliable measurements of
hydraulic vulnerability even on long-vesseled species, especially when
using a small pressure sleeve with no open vessels (Ennajeh \emph{et al}
., 2011). The maximum hydraulic conductivity (\emph{K} \textsubscript{h}
\textsubscript{max}) was calculated as dividing the volume flow rate of
water flowing through the stem by the hydrostatic pressure gradient
along the stem (Johnson \emph{et al} ., 2018). Xylem specific
conductivity (\emph{K} \textsubscript{S}) was calculated as dividing
\emph{K} \textsubscript{h} by the cross-sectional area of the xylem
section being measured. During the measurement period, the laboratory
temperature was maintained at 20 \selectlanguage{english}, and the solutions (10 mM KCl and 1
mM CaCl\textsubscript{2}) used in the measurement were fully vented.

After determining \emph{K} \textsubscript{h} \textsubscript{max}, a stem
was placed in a double-ended pressure sleeve (8 cm in length) and
pressurized for 5 min. Then, the stem was removed from the pressure
sleeve, and \emph{K} \textsubscript{h} was measured using the same
method used for \emph{K} \textsubscript{h} \textsubscript{max}. This
process was repeated at 0.5 to 1 MPa increments (depending on species
and organ) of increasing pressure until \emph{K} \textsubscript{h} had
fallen to less than 10\% of its maximum value. The percentage loss in
hydraulic conductivity (PLC) was calculated as PLC = (1 -\emph{K}
\textsubscript{h} / \emph{K} \textsubscript{hmax}) x 100\%. The pressure
causing 50\% loss of conductivity (\emph{P} \textsubscript{50}) was
calculated following Pammenter \& Willigen (1998) as PLC = 100 / (1 +
exp (\emph{s} / 25 x (\emph{P} \textsubscript{i} - \emph{P}
\textsubscript{50})), where\emph{s} (\% MPa\textsuperscript{-1}) is the
negative slope of the curve at the inflection point, and \emph{P}
\textsubscript{i} is the xylem pressure.

\textbf{2.3 Xylem vessel structural traits}

We measured hydraulically weighted diameters (\emph{D}
\textsubscript{h}) of xylem vessels in the branches and roots. Three
transverse sections (20-30 \selectlanguage{greek}μ\selectlanguage{english}m thick) per tree (total \emph{n} = 90) were
cut from the branch and root segments using a sliding microtome
(SM2010R; Leica, Wetzlar, Germany). The sections were stained with
safranine, rinsed in deionized water, and mounted in glycerol on a glass
slide. For each section, six to ten images per cross-section were taken
at 100\selectlanguage{ngerman}× magnification using a light microscope (DM2500; Leica, Wetzlar,
Germany) equipped with a high-resolution digital camera (ICC50W; Leica,
Wetzlar, Germany). These images were combined to produce an integrated
image covering the whole cross-section (or at least a radial transect
based on the sectional quality), using graphics software (Photoshop CS6;
Adobe Systems Incorporated, USA). A minimum of 300 vessels per section
were analyzed. After the images were downloaded into a computer, we
measured the lumen area of all intact vessels using image analysis
software (ImageJ; National Institutes of Health, USA). Vessel diameter
(\emph{D} , in \selectlanguage{greek}μ\selectlanguage{english}m) was determined as the diameter of a circle with an
area equal to the lumen area. We calculated the hydraulically weighted
mean diameter (\emph{D} \textsubscript{h}, in \selectlanguage{greek}μ\selectlanguage{english}m) for each segment as
([?]\emph{D} \textsuperscript{4}/\emph{N} )\textsuperscript{1/4}
(Tyree\emph{et al} ., 1994), where \emph{N} is the total number of
vessels in an integrated image.

For pairs of vessels, we measured maximum span across a vessel (\emph{b}
) and double wall thickness (\emph{t} ) on digital images using ImageJ,
so that \emph{t} \textsuperscript{2}/\emph{b} \textsuperscript{2}was
proportional to vessel wall reinforcement to resist cavitation (Hacke
\emph{et al} ., 2001). The\emph{t} \textsuperscript{2}/\emph{b}
\textsuperscript{2} value was calculated from the mean of a minimum of
10 vessel pairs per branch or root. We measured the above traits on the
same segment used to measure vulnerability curves.

\textbf{2.4 Environmental data}

Site location data (latitude and longitude) were extracted from source
publications. All climatic variables and soil properties were extracted
from databases using the function `extract' in R 4.1.3 (R Core Team,
2022) package RASTER (Hijmans \emph{et al} ., 2015) (Table S1). We
included 18 variables from WorldClim: mean annual temperature (MAT),
seasonality of temperature (Ts), the highest and lowest temperatures of
the warmest and coldest month (Tmax, Tmin), mean temperature of the
wettest and driest (Twet, Tdry) and warmest and coldest (Twarm, Tcold)
quarters, mean annual precipitation (MAP), seasonality of precipitation
(Ps), the monthly precipitation of the warmest and coldest month (Pmax,
Pmin), precipitation of the wettest and driest (Pwet, Pdry) and warmest
and coldest (Pwarm, Pcold) quarters, mean diurnal range (MDR), and
isothermality (ISO). We extracted potential evapotranspiration (PET) and
aridity index (AI = MAP/PET) using global potential evapotranspiration
(Global-PET) and aridity index (Global-Aridity) data sets
(http://www.cgiar-csi.org). We also extracted potential hydrogen content
of the soil (pH), total soil nitrogen concentration (SN), and total soil
phosphorus concentration (SP) from the SoilGrid
(https://www.isric.org/explore/soilgrids).

\textbf{2.5 Data analyses}

All the data analyses were performed in R 4.1.3 (R Core Team, 2022), and
all the figures were created using SigmaPlot 14.0. Prior to data
analyzed, we natural log-transformed continuous variables to homogenize
variance, except for temperature data because they may contain negative
values. We used absolute values of \emph{P} \textsubscript{50} for
transformation because the original values were negative (Liu \emph{et
al} ., 2021). The differences in hydraulic and anatomical traits between
roots and branches were tested using Welch's \emph{t} -test
(Delacre\emph{et al} ., 2017). Pearson correlation and linear regression
were used to analyze the correlation between root and branch traits.
One-way analysis of variance (ANOVA) was used to test the differences in
mean\emph{P} \textsubscript{50 root - branch} for species across
different biomes. Multiple regression and stepwise regression were used
to select the appropriate factors for analyses (Hector \emph{et al} .,
1999). Redundancy analysis (RDA; Braak, 1986) was used to determine the
relative importance of environmental factors explaining hydraulic
segmentation between roots and branches. Because of the
multicollinearity among the environmental factors, we used variation and
hierarchical partitioning (Chevan \& Sutherland, 1991) and estimated
unique and shared contributions among the factors. RDA analysis was
performed using the rdacca.hp package (Lai \emph{et al} ., 2022). The
`rdacca.hp' function was used in variation and hierarchical
partitioning. The `permu.hp' function was implemented significance
testing for individual environmental factor contribution to explain
variation of \emph{P} \textsubscript{50 root - branch}. For all
analyses, the significant effects were inferred at \emph{P} \textless{}
0.05.

\textbf{Results}

\textbf{3.1 Differences in xylem hydraulic and structural traits between
branches and roots}

\emph{P} \textsubscript{50 root} and \emph{P} \textsubscript{50
branch}were positively correlated (\emph{r} \textsuperscript{2} = 0.72,
\emph{P}\textless{} 0.001), with roots being more vulnerable to
cavitation than branches across a 96\% of the studied species (Figure
1a). Compared with roots, 94\% of the studied species showed
higher\emph{K} \textsubscript{S branch} values (Figure 1b). In addition,
79\% of the species had higher\emph{t} \textsuperscript{2}/\emph{b}
\textsuperscript{2} values in branches than in roots (Figure 1c), and
about 98\% of the species had roots with wider hydraulically weighted
vessel or tracheid diameter than branches (Figure 1d). Additionally, all
these root hydraulic and xylem anatomical traits were positively
associated with the ones of the branch (Fig 1).

\textbf{3.2 Difference in structure-function relationships between roots
and branches}

\emph{t} \textsuperscript{2}\emph{/b} \textsuperscript{2} and
\textbar{}\emph{P} \textsubscript{50}\textbar{} were positively
correlated both in branches (\emph{r} \textsuperscript{2} =
0.50,\emph{P} \textless{} 0.001; Figure 2a) and in roots (\emph{r}
\textsuperscript{2} = 0.60, \emph{P} \textless{} 0.001; Figure 2b). By
contrast, there were significantly negative correlations between\emph{D}
\textsubscript{h} and \textbar{}\emph{P} \textsubscript{50}\textbar{}
both in branches (\emph{r} \textsuperscript{2} = 0.50, \emph{P}
\textless{} 0.001; Figure 2c) and in roots (\emph{r} \textsuperscript{2}
= 0.31, \emph{P}\textless{} 0.001; Figure 2d), whereas the regression
coefficient of root and branch traits had no significant differences
(\emph{P} = 0.424).

\textbf{3.3 Relationships between \emph{P}\textsubscript{50 root -
branch} and hydraulic traits of xylem}

There was a significantly positive correlation between\emph{P}
\textsubscript{50 branch} and \emph{P} \textsubscript{50 root - branch}
(\emph{r} \textsuperscript{2} = 0.31, \emph{P} \textless{} 0.001; Figure
3a). However, the correlation between \emph{P} \textsubscript{50 root}
and \emph{P} \textsubscript{50 root - branch} was not significant
(\emph{r} \textsuperscript{2} = 0.02, \emph{P} \textless{} 0.05; Figure
3b). Both \emph{K} \textsubscript{S branch} and \emph{K}
\textsubscript{S root} had significant correlations with \emph{P}
\textsubscript{50 root - branch} (\emph{r} \textsuperscript{2} = 0.14
and 0.09, respectively,\emph{P} \textless{} 0.001; Figure 3c, 3d). In
general, xylem specific hydraulic conductivity (\emph{K}
\textsubscript{S}) decreased with an increase of hydraulic vulnerability
segmentation between roots and branches, but \emph{P} \textsubscript{50}
increased with\emph{P} \textsubscript{50 root - branch} (Figure 3).

\textbf{3.4 Environmental variables affect hydraulic vulnerability
segmentation}

Multiple regression models with all environment variables considered,
explained up to 21.7\% of the variation in \emph{P} \textsubscript{50
root -- branch}, and a subset of environmental variables identified in
stepwise regression models explained 25.7\% of the variation.
Hierarchical variance partitioning suggested that aridity Index (AI),
Precipitation of Wettest Quarter (Pwet), Mean Annual Precipitation (MAP)
and Precipitation Seasonality (Ps) had the strongest contribution
to\emph{P} \textsubscript{50 root - branch} and explained 68.3\% of the
total variation (Table 1). AI, with the highest individual contribution
proportion (24.4\%), was the major driver of the variation in\emph{P}
\textsubscript{50 root - branch}, followed by Pwet (17.7\%) and MAP
(15.5\%). Environmental factors associated with temperature including
Mean annual temperature (MAT), temperature seasonality (Ts), max
temperature of the warmest month (Tmax), min temperature of the coldest
month (Tmin) and mean temperature of the coldest quarter (Tcold)
explained 14.0\% of the total variation in \emph{P} \textsubscript{50
root -- branch} (Table 1). MAT had the lowest individual contribution
proportion (1.3\%). Several other environmental variables, including
isothermality, total soil nitrogen concentration (SN), explained smaller
portions (9.9\%) of the total variation in \emph{P} \textsubscript{50
root - branch} than other factors (Table 1).

\textbf{3.5 AI drives the hydraulic function by affecting xylem
anatomical traits}

The value of \emph{P} \textsubscript{50 root - branch} decreased
significantly with AI (\emph{r} \textsuperscript{2} = 0.14,
\emph{P}\textless{} 0.001; Figure 4a) and varied significantly (one-way
ANOVA,\emph{F} = 18.5, \emph{P} \textless{} 0.001) across four biomes
(tropical seasonal forest, temperate seasonal forest,
woodland/shrubland, and desert). On average, the species from desert had
the highest \emph{P} \textsubscript{50 root - branch} (3.06 MPa), while,
species from tropical seasonal forests had the lowest (0.87 MPa).
Notably, the variation in \emph{P} \textsubscript{50 root -
branch}increased from the most humid biome (i.e., tropical seasonal
forests) to driest (i.e., desert). The trend lines fitted between AI and
theanatomical traits of branches and roots had significantly different
slopes and intercepts (Figure 4b, 4c). There was a significantly
positive correlation between \emph{D} \textsubscript{h} and AI in
branches (\emph{r} \textsuperscript{2} = 0.12, \emph{P} \textless{}
0.01; Figure 4c), while the correlation between \emph{D}
\textsubscript{h} and AI in roots was not significant (\emph{r}
\textsuperscript{2} = 0.00,\emph{P} \textgreater{} 0.05).

\textbf{4. Discussion}

In the present study, we compared hydraulic and anatomical traits of
woody species from different biomes, tested the vulnerability
segmentation hypothesis (i.e., plant branches are more resistant to
cavitation than their roots, \emph{P} \textsubscript{50 root - branch}
\textgreater{} 0), and analyzed the coordination between \emph{P}
\textsubscript{50 root - branch} and environmental variables, so as to
provide new insights into how environment factors drive the
vulnerability segmentation between branches and roots. Our results
showed that \emph{P} \textsubscript{50 root - branch} \textgreater{} 0
was common across different biomes (tropical seasonal forest, temperate
seasonal forest, woodland/shrubland, and desert), which are consistent
with the vulnerability segmentation hypothesis. Moreover, the degree of
vulnerability segmentation between roots and branches increased with
environmental aridity, which may be attributed to the decoupling of root
and branch xylem anatomical traits, e.g., hydraulically weighted
diameters (\emph{D} \textsubscript{h}) increased significantly with
aridity in branches but not in roots.

\textbf{4.1 Higher vulnerability of roots compared to branches}

We observed a notable increase in xylem resistance to cavitation
(\emph{P} \textsubscript{50}), but a decline in hydraulic efficiency
(\emph{K} \textsubscript{S}) from roots to branches. Roots were more
vulnerable to cavitation than branches for a given xylem diameter, and
roots had 62\% higher (less negative) \emph{P} \textsubscript{50} value
than branches (Figure 1). These results implied that roots with lower
hydraulic safety, might be more susceptible to drought stress than
branches along plant water transport pathway from soil to the atmosphere
(Jackson \emph{et al} ., 2000). However, roots with higher hydraulic
efficiency than branches ensuring the water supply of the whole plant
and prevent water deficit caused by high transpiration
(Rodriguez-Dominguez \& Brodribb, 2020). This difference in xylem
cavitation vulnerability between branches and roots is matched by
variability in wood anatomical characteristics, in that we found a lower
vessel wall reinforcement (\emph{t} \textsuperscript{2}\emph{/b}
\textsuperscript{2}) and a wider hydraulically weighted mean diameter
(\emph{D} \textsubscript{h}) in root xylem compared to branch xylem
(Figure 1), and these anatomical traits showed significant correlations
with \emph{P} \textsubscript{50} both in roots and branches (Figure 2).
Not only to ensure anchorage, woody species commonly require roots to
absorb and transport water with mineral nutrients, wider conduits are
therefore needed to improve efficiency of transportation in roots.
However, branches have to resist stronger mechanical stress induced by
gravity, wind, and ice loading etc., thus they need a relatively more
robust woody structure than roots (MacFarlane \& Kane, 2017). It has
been shown at the cellular level, branches tended to have thicker cell
walls and conduits with higher\emph{t} \textsuperscript{2}\emph{/b}
\textsuperscript{2} than roots, which are often associated with lower
conducting efficiency but greater resistance to cavitation (Hacke \&
Jansen, 2009; Schulte \emph{et al} ., 2015).

4.2 \textbf{Relationships between xylem hydraulic and anatomical traits}

Our results suggest a generally positive relationship between\emph{D}
\textsubscript{h} and \emph{P} \textsubscript{50} (Barotto\emph{et al}
., 2018; Jacobsen \emph{et al} ., 2019). However, cavitation formation
may not be directly related to conduit size, but pit properties (Lemaire
\emph{et al} ., 2021; Levionnois \emph{et al} ., 2021). The pit area
hypothesis (also called `rare pit' hypothesis) proposed that the larger
conduits tend to have a greater pitted wall area, which is more
vulnerable to cavitation (Mrad \emph{et al} ., 2018). Moreover, thicker
cell walls may also influence pit morphology, including the pit
membrane, thereby increasing xylem cavitation resistance (Li \emph{et
al} ., 2016). Obviously, the pit membrane area is at least partly
affected by conduit dimensions (Hacke \emph{et al} ., 2017), suggesting
that root xylem with larger conduits and thinner cell walls than those
of branches, may be more vulnerable to drought-induced collapsing stress
or cavitation.

The vulnerability segmentation between branches and roots may be an
adaptive strategy for woody plants to cope with drought stress.
Localized failure in relatively cheap and replaceable units (e.g., roots
and leaves) would avoid disruption of major arteries of axial transport
(Klepsch \emph{et al} ., 2018; Aritsara \emph{et al} ., 2022). In this
study, we found a positive correlation between \emph{P}
\textsubscript{50 root - branch} and \emph{P} \textsubscript{50 branch}
(Figure 3a), but no correlation was found between \emph{P}
\textsubscript{50 root - branch}and \emph{P} \textsubscript{50 root}
(Figure 3b). These results implied that the degree of vulnerability
segmentation between roots and branches is likely to be dominantly
affected by the changes in cavitation vulnerability of branches. By
contrast, roots generally maintain a relatively lower vulnerability to
cavitation (Figure 3b). This may due to the fact that coarse roots (root
diameter \textgreater{} 5 mm) are not easily exposed to very negative
water potential. During drought exposure, cortical lacunae formation
caused the outside xylem hydraulic conductivity within the cortical
cells declined earlier before cavitation formed in coarse roots (Cuneo
\emph{et al} ., 2021). Cavitation formation in fine roots (root diameter
\textless{} 2 mm) would interrupt the soil-root-connectivity during
drought, thereby restricting hydraulic failure to replaceable units.
(Carminati \& Javaux, 2020; Aritsara \emph{et al} ., 2022). Moreover,
coarse roots may also act as hydraulic fuses like fine roots, which form
a multi-stage circuit breaker with fine roots together to protect the
hydraulic safety of the organs with high carbon investment (e.g., main
root and trunk) against potentially extreme drought. We found woody
species with branches that are more resistant to cavitation also have a
greater degree of vulnerability segmentation (Figure 3a). However, this
may be at the cost of partial loss of xylem hydraulic conductivity. We
also found that \emph{K} \textsubscript{S} in both roots and branches
were markedly lower in woody species with greater degree of vulnerable
segmentation (Figure 3c, 3d), highlighting a trade-off between hydraulic
safety (capacity to resist cavitation) and efficiency (capacity to
transport water) in the xylem of branches and roots (Gleason \emph{et
al} ., 2016).

\textbf{4.3 Increasing \emph{P}\textsubscript{50 root - branch} with
increasing aridity}

We found that precipitation-related environmental variables (e.g., AI,
Pwet, MAP and Ps) showed more explanatory power than others, indicating
the importance of moisture in driving the shift of\emph{P}
\textsubscript{50 root - branch}, with AI that reflects
evapotranspiration and precipitation status of the study sites, being
the major environmental factor in explaining the variation of\emph{P}
\textsubscript{50 root - branch}. Moreover, we found notable changes in
\emph{P} \textsubscript{50 root - branch} (on average) across different
biomes (Figure 4a). In particular, \emph{P} \textsubscript{50 root -
branch} decreased or even became negative with increasing AI, which is
consistent with the pattern observed between branches and leaves in a
previous study (Zhu, \emph{et al} ., 2016). In general, the
vulnerability segmentation is more pronounced for species from arid
regions compared to humid habitats (i.e., showing lower\emph{P}
\textsubscript{50 root - branch} values). Notably, the vulnerability
segmentation might be advantageous for woody species from arid
environments, where survival during protracted drought may depend on
isolating a part of roots from drying soils (Johnson \emph{et al} .,
2016). By contrast, woody species in wetter environments displayed a
relatively lower degree of vulnerability segmentation, because abundant
water availability in wetter environments might have relieved the
vulnerability segmentation pressure. A relatively higher hydraulic
conductivity both in branches and roots for species from humid
environments might help them to absorb and transport water more
efficiently, hence keeping water balance of the whole plant. Besides, a
greater plant hydraulic conductivity might contribute to lower water
potential gradients from soil to the terminal roots, resulting in a
lower risk of cavitation in both roots and branches (Wang \emph{et al}
., 2022).

\textbf{4.4 AI drives hydraulic vulnerability segmentation by affecting
xylem structure}

Increasing vulnerability to cavitation along the hydraulic pathway from
roots to branches is likely affected by the diameter and the physical
properties of the xylem conduits. Our results identified the decoupling
responses of root and branch structural traits to environmental aridity
(i.e., AI), which may be an important greason for determining the
vulnerability segmentation. We found an apparent decline of\emph{D}
\textsubscript{h} in branches with increasing AI, suggesting conduit
diameter in branch xylem be constrained by environmental aridity
(Rodr\selectlanguage{ngerman}íguez-Ramírez \emph{et al} ., 2020, Fontes \emph{et al} ., 2022).
By contrast, the relationship between \emph{D} \textsubscript{h} and AI
in roots was non-significant. Previous studies had reported that root
cortical cells can reduce hydraulic conductivity by forming cortical
lacunae before embolism (Cuneo \emph{et al} ., 2016; Carminati \&
Javaux, 2020). This may help roots to avoid exposure to negative water
potential in face of drought, and root hydraulic conductivity is likely
to be restored rapidly after re-wetting (Bourbia \emph{et al} ., 2021).
Under water-limited conditions, tolerating drought stress while
maintaining hydraulic function requires modification of hydraulic
architecture, including increased the relative number of narrow conduits
(Brodribb\emph{et al} ., 2012; Yao \emph{et al} ., 2021). However,
plants in humid habitats tend to grow taller, which requires more water
transporting to the top of crowns to sustain transpiration and
photosynthesis (Koch\emph{et al} ., 2004; Chin \emph{et al} ., 2022). A
network composed of wide conduits over a large distance of the vertical
transport pathway would theoretically meet these requirements. This is
consistent with our current observations that as the available water
increases, plants choose to widen the diameter of vessels or tracheids
downstream (i.e., in branches) of the water transport pathway,
therefore, matches the usually wider conduits upstream (i.e., in roots)
of the pathway, to enable their branches to get stronger competitiveness
for light and carbon uptake (Pfautsch \emph{et al} ., 2016). The safety
versus efficiency trade-off theory in the xylem claimed that when the
tension to pull sufficient water and transport through the stem exceeds
the physical limits, the risk of cavitations in the xylem of branches
will inevitably increase (Tyree \& Zimmermann, 2002; Aritsara \emph{et
al} ., 2021; Wang \emph{et al} ., 2022). This will gradually reduce the
gap with the cavitation vulnerability in roots, resulting in the
decrease of\emph{P} \textsubscript{50 root - branch} along environmental
aridity gradients.

\textbf{Conclusions}

Our results demonstrates that hydraulic vulnerability segmentation
between branches and roots (\emph{P} \textsubscript{50 root -
branch}\textgreater{} 0) is common for species from arid environments.
Such a segmentation has been proved important for plant drought
adaptation, with roots functioning as ``hydraulic fuses'' in the water
transport pathway. However, species from humid habits display a lower
degree or even a lack of the segmentation. These species may opt for
more radical hydraulic strategies (e.g., lower cavitation vulnerability
and higher hydraulic conductivity) to adapt to humid conditions. Along
the environmental aridity gradients, xylem structure in branches changes
from ``more efficient'' (e.g., wider conduit, higher hydraulic
conductivity) to ``safer'' (e.g., narrower conduit, more negative
\emph{P} \textsubscript{50}). By contrast, root xylem tends to maintain
hydraulic efficiency. Therefore, by identifying both environmental
drivers and structural basis of vulnerability segmentation between
branches and roots, our results provide a novel ecological perspective
to better understand hydraulic vulnerability segmentation in woody
plants.

\textbf{Acknowledgements}

This work was funded by the National Natural Science Foundation of China
(31825005, 32171503), the Key Special Project for Introduced Talents
Team of Southern Marine Science and Engineering Guangdong Laboratory
(Guangzhou; GML2019ZD0408), and Institution of South China Sea Ecology
and Environmental Engineering, Chinese Academy of Sciences
(ISEE2020YB01). The authors declare no conflict of interest.

\textbf{Conflict of interest statement}

All authors disclosed no relevant relationships.



\textbf{CRediT author contributions statement}

Weize Tang: Conceptualization, Investigation, Formal analysis, Writing -
Original draft preparation, Visualization, Data Curation.

Hui Liu: Formal analysis, Software, Validation, Writing - Review \&
Editing.

Xingyun Liang: Conceptualization, Writing - Review \& Editing, Funding
acquisition.

Pengcheng He: Writing - Review \& Editing.

Han Zhao: Investigation.

Xiaorong Liu: Data Curation.

Qing Ye: Conceptualization, Resources, Project administration, Funding
acquisition, Writing - Review \& Editing.

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\textbf{Figure legends}

\textbf{Figure 1} Relationship of hydraulic and anatomical traits
between branches and roots: (a) \emph{P} \textsubscript{50}, water
potential at a 50\% loss of hydraulic conductivity; (b)\emph{K}
\textsubscript{S}, specific xylem conductivity per unit of
cross-sectional sapwood area; (c)\emph{t} \textsuperscript{2}/\emph{b}
\textsuperscript{2}, vessel wall reinforcement; (d) \emph{D}
\textsubscript{h}, hydraulically weighted mean diameters (the x-axis and
y-axis were natural log-transformed). Dashed line represents the 1:1
relationship. The coefficients of determination (\emph{r}
\textsuperscript{2}) of the simple linear regression models were marked.
Each point denotes a species. Insert: comparison of traits between
branches and roots. Boxes and whiskers represent the minimum, 25th
percentile, median, 75th percentile, and maximum values in each bin,
respectively. Solid points are outliers (data points located outside the
whiskers of the boxplot). Numbers in the brackets are sample sizes. ***
\emph{P} \textless{} 0.001.

\textbf{Figure 2} Relationships between \emph{P}
\textsubscript{50}(water potential at a 50\% loss of hydraulic
conductivity) and anatomical traits (\emph{t}
\textsuperscript{2}/\emph{b} \textsuperscript{2}, vessel wall
reinforcement; \emph{D} \textsubscript{h}, hydraulically weighted mean
diameters) of branches and roots: (a)\emph{t}
\textsuperscript{2}/\emph{b} \textsuperscript{2} in branches,
(b)\emph{t} \textsuperscript{2}/\emph{b} \textsuperscript{2} in roots,
(c)\emph{D} \textsubscript{h} in branches, (d) \emph{D}
\textsubscript{h} in roots (the x-axis and y-axis were natural
long-transformed). Each point denotes a species (branch traits:
\emph{filled dark yellow circles} and root traits: \emph{filled dark red
triangles} ). ***, \emph{P}\textless{} 0.001.

\textbf{Figure 3} Relationships between \emph{P} \textsubscript{50 root
- branch} (hydraulic vulnerability segmentation between root and branch)
and hydraulic traits (\emph{P} \textsubscript{50}, water potential at a
50\% loss of hydraulic conductivity; \emph{K} \textsubscript{S},
specific xylem conductivity per unit of cross-sectional sapwood area) of
branches and roots: (a) \emph{P} \textsubscript{50} of branches,
(b)\emph{P} \textsubscript{50} of roots, (c) \emph{K} \textsubscript{S}
of branches, (d) \emph{K} \textsubscript{S} of roots (the x-axis and
y-axis were natural long-transformed). Each point denotes a species
(branch traits: \emph{filled dark yellow circles} and root traits:
\emph{filled dark red triangles} ). Dashed line in (b) indicates
nonsignificant correlations. Significance: ***, \emph{P} \textless{}
0.001; **, 0.001 \selectlanguage{english}[?]\emph{P} \textless{} 0.01.

\textbf{Figure 4} Comparisons of hydraulic vulnerability segmentation
(\emph{P} \textsubscript{50 root - branch}) and hydraulically weighted
mean diameter (\emph{D} \textsubscript{h}) across different biomes:
(a)\emph{P} \textsubscript{50 root - branch}, (b) \emph{D}
\textsubscript{h}of branches, (c) \emph{D} \textsubscript{h} of roots.
Boxes and whiskers represent the minimum, 25th percentile, median, 75th
percentile, and maximum values in each bin, respectively. Solid points
are outliers defined as data points located outside the whiskers of the
boxplot. The horizontal short dashed line indicates that \emph{P}
\textsubscript{50 root - branch} is equal to zero (a). Mean values of AI
for different biomes are shown (a). The numbers in the brackets are
sample sizes. Different letters indicate statistically significant
differences at\emph{P} \textless{} 0.05 (one-way ANOVA). Inset:
relationships between\emph{P} \textsubscript{50 root - branch} and
aridity index (AI) (a), relationships between \emph{D} \textsubscript{h}
and AI of branches (b) and roots (c) (the x-axis and y-axis were natural
log-transformed) across biomes. Each point denotes one species
{[}\emph{filled green circles} , tropical seasonal forest (TRS);
\emph{filled aquamarine circles} , temperate seasonal forest (TMS);
\emph{filled dodger blue circles} , woodland and shrubland (WDS); and
\emph{filled purple circles} , desert (DES){]}. Dashed line in (c)
indicates nonsignificant models. Significance: ***, \emph{P} \textless{}
0.001; **, 0.001 \selectlanguage{english}[?]\emph{P} \textless{} 0.01.

\textbf{Supporting Information}

\textbf{Table S1} Full dataset and data sources in this study, with
definitions and descriptions for each index. (Provided in a separate
excel file)

\textbf{Table S2} Basic characteristics of ten evergreen trees measured
in this study, including diameter at breast height (DBH, cm), sample
diameter of branch (SDB, cm), sample diameter of root (SDR, cm), sample
length of branch (SLB, cm) and sample length of root (SLR, cm)

\textbf{Table}

\textbf{Table 1} Selected outputs of variation partitioning and
hierarchical partitioning from canonical ordination analysis of\emph{P}
\textsubscript{50 root - branch} and environmental factors.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Variables\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Unique\textsuperscript{a}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Average.share\textsuperscript{b}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Average.share\textsuperscript{b}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Individual importance\textsuperscript{c}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
I.perc (\%) \textsuperscript{d}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Significance\textsuperscript{e}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Aridity Index\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.094\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.031\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.031\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.063\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{24.4}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Precipitation of Wettest Quarter\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.100\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.055\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.055\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.045\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{17.7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Annual Precipitation\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.065\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.025\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.025\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.040\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{15.5}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Precipitation Seasonality\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.118\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.095\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.095\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.023\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{9.0}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Isothermality\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.102\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.086\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.086\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.017\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{6.5}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Precipitation of Coldest Quarter\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.076\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.061\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.061\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.015\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{5.9}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Temperature Seasonality\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.093\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.083\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.083\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.010\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{4.1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Min Temperature of Coldest Month\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.054\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.045\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.045\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.010\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{3.8}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Total Soil Nitrogen Concentration\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.082\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.073\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.073\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.009\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{3.4}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Max Temperature of Warmest Month\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.050\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.043\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.043\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.008\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{2.9}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mean Temperature of Coldest Quarter\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.060\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.032\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.032\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.004\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{1.9}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mean Annual Temperature\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.060\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.029\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.029\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.003\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{1.3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Precipitation of Driest Quarter\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.023\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.019\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.006\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.006\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{1.7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.977\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.720\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.257\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.257\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
100\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a}Unique contributions of environmental variables; the
sum is total \emph{r} \textsuperscript{2} from multiple regression.

\textsuperscript{b}Total average shared effects with other variables.

\textsuperscript{c}Individual importance of environmental variables; the
sum is adjusted \emph{r} \textsuperscript{2} from multiple regression.

\textsuperscript{d}Individual effect divided by total adjusted\emph{r}
\textsuperscript{2} from column individual importance.

\textsuperscript{e}Significance of environmental variables. ***,\emph{P}
\textless{} 0.001; **, 0.001 [?] \emph{P} \textless{} 0.01; *, 0.01 [?]
\emph{P} \textless{} 0.05.

\textbf{Table S2} Basic characteristics of ten evergreen trees sampled
in this study. The numbers are mean \selectlanguage{ngerman}± SE, \emph{n} = 3. DBH, diameter at
breast height (cm); SDB, sample diameter of branch (mm); SDR, sample
diameter of root (mm); SLB, sample length of branch (cm); SLR, sample
length of root (cm).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Species & DBH & SDB & SDR & SLB & SLR\tabularnewline
\midrule
\endhead
\emph{Castanopsis fissa} & 32.00 \selectlanguage{ngerman}± 1.31 & 5.87 \selectlanguage{ngerman}± 0.46 & 5.28 \selectlanguage{ngerman}± 0.39 &
71.23 \selectlanguage{ngerman}± 0.86 & 75.87 \selectlanguage{ngerman}± 0.49\tabularnewline
\emph{Aidia canthioides} & 29.27 \selectlanguage{ngerman}± 1.55 & 5.91 \selectlanguage{ngerman}± 0.47 & 6.75 \selectlanguage{ngerman}± 1.34 &
78.76 \selectlanguage{ngerman}± 1.67 & 80.89 \selectlanguage{ngerman}± 0.41\tabularnewline
\emph{Cryptocarya concinna} & 27.73 \selectlanguage{ngerman}± 2.07 & 6.58 \selectlanguage{ngerman}± 0.54 & 6.56 \selectlanguage{ngerman}± 0.44 &
15.65 \selectlanguage{ngerman}± 0.42 & 18.12 \selectlanguage{ngerman}± 0.86\tabularnewline
\emph{Carallia brachiata} & 27.77 \selectlanguage{ngerman}± 1.92 & 7.10 \selectlanguage{ngerman}± 0.35 & 7.49 \selectlanguage{ngerman}± 0.87 &
42.82 \selectlanguage{ngerman}± 1.64 & 44.26 \selectlanguage{ngerman}± 0.08\tabularnewline
\emph{Castanopsis chinensis} & 33.30 \selectlanguage{ngerman}± 2.21 & 6.22 \selectlanguage{ngerman}± 0.42 & 7.39 \selectlanguage{ngerman}± 1.61
& 78.27 \selectlanguage{ngerman}± 1.98 & 73.73 \selectlanguage{ngerman}± 3.50\tabularnewline
\emph{Memecylon ligustrifolium} & 32.00 \selectlanguage{ngerman}± 1.71 & 6.55 \selectlanguage{ngerman}± 0.11 & 4.75 \selectlanguage{ngerman}±
0.37 & 21.97 \selectlanguage{ngerman}± 0.31 & 25.06 \selectlanguage{ngerman}± 0.66\tabularnewline
\emph{Acmena acuminatissima} & 35.07 \selectlanguage{ngerman}± 1.88 & 7.09 \selectlanguage{ngerman}± 0.55 & 7.39 \selectlanguage{ngerman}± 0.51
& 50.89 \selectlanguage{ngerman}± 0.88 & 54.69 \selectlanguage{ngerman}± 0.38\tabularnewline
\emph{Lindera chunii} & 25.77 \selectlanguage{ngerman}± 1.16 & 7.21 \selectlanguage{ngerman}± 0.29 & 5.31 \selectlanguage{ngerman}± 0.87 & 31.22
\selectlanguage{ngerman}± 1.65 & 36.09 \selectlanguage{ngerman}± 1.74\tabularnewline
\emph{Schima superba} & 35.37 \selectlanguage{ngerman}± 1.55 & 5.95 \selectlanguage{ngerman}± 0.20 & 4.45 \selectlanguage{ngerman}± 0.10 & 32.58
\selectlanguage{ngerman}± 1.23 & 41.37 \selectlanguage{ngerman}± 0.85\tabularnewline
\emph{Craibiodendron scleranthum} & 35.77 \selectlanguage{ngerman}± 1.79 & 6.86 \selectlanguage{ngerman}± 1.01 & 7.57 \selectlanguage{ngerman}±
0.89 & 19.73 \selectlanguage{ngerman}± 0.28 & 31.96 \selectlanguage{ngerman}± 1.12\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1/Fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2/Fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3/Fig3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4/Fig4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

