\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,greek,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Structured and unstructured intraspecific propagule trait variation
across environmental gradients in a widespread mangrove}



\author[1]{Jingjun Yang}%
\author[2]{Anchi Wu}%
\author[1]{Jinhua Li}%
\author[1]{Haihang Wei}%
\author[1]{Jie Qin}%
\author[1]{Hongdeng Tian}%
\author[3]{Donghan Fan}%
\author[3]{Weidai Wu}%
\author[4]{Shan Chen}%
\author[5]{Xin Tong}%
\author[1]{Xiu Liu}%
\affil[1]{Guangxi Forestry Research Institute}%
\affil[2]{South China Botanical Garden}%
\affil[3]{Qinzhou Forestry Research Institute}%
\affil[4]{East China Normal University}%
\affil[5]{Shanghai Chen Shan Botanical Garden}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Increasing studies have shown the importance of intraspecific trait
variation (ITV) on the ecological process. However, the patterns and
sources of ITV are still unclear, especially in the propagule of coastal
vegetation. Here, we measured fresh weight (FW), fresh length (FL),
maximum transverse diameter (TDmax), minimum transverse diameter (TDmin)
and the ratio of TDmax to TDmin (RTD) of the hypocotyl (propagule) of
Kandelia obovata for 66 genealogies across 26 sites. By combining
multiple factors of climate, ocean and maternal tree to analyze their
effects on the intraspecific trait variation of mangrove hypocotyl. The
results showed that value of establishment traits (FW, FL, TDmax and
TDmin which are related to mass) decreased along increasing latitudinal
gradients and they were directly positively regulated by temperature.
ITV of dispersal trait (RTD) was unstructured along latitudinal
gradients, which was constrained by fitness tradeoff. Our findings
indicate that establishment traits mainly varied between populations,
whereas dispersal traits mainly varied between individuals. This study
provides insights into the ITV of propagule in different functional
dimensions on a broad scale and that may help integrate ITV into future
analyses of mangrove protection.%
\end{abstract}%



\sloppy


\textbf{Title:} Structured and unstructured intraspecific propagule
trait variation across environmental gradients in a widespread mangrove

\textbf{Running Head:} Variation of mangrove propagule traits

\textbf{Jingjun Yang\textsuperscript{1+}, Anchi
Wu\textsuperscript{2,3+}, Jinhua Li\textsuperscript{1}, Haihang
Wei\textsuperscript{1}, Jie Qin\textsuperscript{1}, Hongdeng
Tian\textsuperscript{1}, Donghan Fan\textsuperscript{4}, Weidai
Wu\textsuperscript{4}, Shan Chen\textsuperscript{5}, Xin
Tong\textsuperscript{6*}, Xiu Liu\textsuperscript{1*}}

\textsuperscript{1} Guangxi Forestry Research Institute, Nanning 530002,
China

\textsuperscript{2} Key Laboratory of Vegetation Restoration and
Management of Degraded Ecosystem, South China Botanical Garden, Chinese
Academy of Sciences, Guangzhou 510650, China

\textsuperscript{3} College of Resources and Environment, University of
Chinese Academy of Sciences, Beijing 100049, China

\textsuperscript{4} Qinzhou Forestry Research Institute, Qinzhou 535012,
China

\textsuperscript{5} School of Ecological and Environmental Sciences,
East China Normal University, Shanghai 200241, China

\textsuperscript{6} Eastern China Conservation Centre for Wild
Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai
201602, China

+ These authors contributed equally

* Correspondence: xiuliu1010@126.com (Xiu Liu); txoinng@163.com (Xin
Tong)

\textbf{Abstract}

Increasing studies have shown the importance of intraspecific trait
variation (ITV) on the ecological process. However, the patterns and
sources of ITV are still unclear, especially in the propagule of coastal
vegetation. Here, we measured fresh weight (FW), fresh length (FL),
maximum transverse diameter (TD\textsubscript{max}), minimum transverse
diameter (TD\textsubscript{min}) and the ratio of
TD\textsubscript{max}to TD\textsubscript{min} (RTD) of the hypocotyl
(propagule) of\emph{Kandelia obovata} for 66 genealogies across 26
sites. By combining multiple~factors of climate, ocean and maternal tree
to analyze their effects on the intraspecific trait variation of
mangrove hypocotyl. The results showed that value of establishment
traits (FW, FL, TD\textsubscript{max} and TD\textsubscript{min} which
are related to mass) decreased along increasing latitudinal gradients
and they were directly positively regulated by temperature. ITV of
dispersal trait (RTD) was unstructured along latitudinal gradients,
which was constrained by fitness tradeoff. Our findings indicate that
establishment traits mainly varied between populations, whereas
dispersal traits mainly varied between individuals. This study provides
insights into the ITV of propagule in different functional dimensions on
a broad scale and that may help integrate ITV into future analyses of
mangrove protection.

\textbf{Keywords}

dispersal, intraspecific trait variation, \emph{Kandelia obovate} ,
mangrove, propagule, tradeoff

\textbf{1 Introduction}

Despite being recognized as a foundation for the theory of evolution by
natural selection, the importance of intraspecific trait variation (ITV)
has been neglected over time in ecology (Bolnick et al., 2011).
Recently, there has been a resurgence of ecological interest in ITV,
stimulated by the proliferating studies that underline the tremendous
effects of ITV on community assembly and ecosystem functioning (Violle
et al., 2012; Cardou et al., 2022). However, the patterns and sources of
ITV in themselves are still unclear (Cope et al., 2022). For example,
ITV can be structured at various spatial scales in relation to different
drivers. Between-population ITV tends to be shaped by large-scale
environmental gradients, whereas within-population ITV is more likely
the consequence of heritable differences and/or plastic responses to the
local environment (Albert et al. 2010; Martin et al., 2017).
Alternatively, ITV can be unstructured if the trait is mainly determined
by stochastic processes or constrained by fitness tradeoffs (Kendall \&
Fox, 2003; Moran et al., 2016). Elucidating ITV distribution across
multiple scales is yet crucial for understanding and predicting
ecological responses to global changes (Cochrane et al., 2015; Moran et
al., 2016).

Trait-based plant studies to date have largely focused on vegetative
traits (e.g., leaf and root functional traits), with less attention
placed on seed traits despite the significance of seed traits for plant
regeneration and long-term persistence (Saatkamp et al., 2019).
Knowledge of seed traits is particularly urgent for mangroves, which
once covered over 200,000 km\textsuperscript{2} of sheltered tropical
and subtropical coastlines but nowadays has been disappearing worldwide
for decades at an extremely high rate (Duke et al., 2007; Friess et al.,
2019; Giri et al., 2015). Due to the property of low species richness
and redundancy, mangrove degradation is always followed by pronounced
losses of ecological multifunctionality (Arifanti et al., 2022; Donato
et al., 2011; Feller et al., 2010). Such situation emphasizes mangrove
recruitment and thereby calls for characterizing seed/propagule traits
that represent key dimensions of the `regeneration niche'
(\emph{sensu}Grubb, 1977) in mangroves (Feller et al., 2010; Peterson \&
Bell, 2012).

Many mangroves (e.g., Rhizophoraceae, Avicenniaceae) have evolved a
special reproductive strategy of viviparous seeds (germinating
precociously while attached to the maternal plant), probably reflecting
an adaptation to the salty and flooding intertidal environments (Feller
et al., 2010). Due to the lack of seed dormancy, mangrove forests
destroyed by severe disturbances such as hurricanes and tsunamis may not
have local seed reserves, necessitating recolonization through
long-distance seed dispersal from relatively undisturbed populations
(Nettel \& Dodd, 2007). This is particularly the case for Rhizophoraceae
forests, as Rhizophoraceae species lack the capacity of resprouting from
damaged trees as Avicenniaceae species (Baldwin et al., 2001).
Therefore, dispersal/retention is a key dimension of propagule functions
where focal traits should be targeted for mangroves (Van der Stocken et
al., 2019). Whether dispersed or retained, establishment is a
prerequisite for any propagules to contribute to the regeneration,
representing another critical dimension of seed trait (Krauss et al.,
2008).

Most ITV studies in mangroves, though few in absolute number, have
investigated propagule traits (e.g., size and weight) in relation to
establishment, rarely considering the dispersal/retention dimension
(Saenger \& West, 2018; Yang et al., 2020; Zhu et al., 2021). These
studies showed that temperature and/or precipitation may have given rise
to structured ITV in mangroves across geographical gradients (Saenger \&
West, 2018; Yang et al., 2020; Zhu et al., 2021), a pattern commonly
reported in terrestrial forests (Kumordzi et al., 2019). Nevertheless,
as mangroves are coastal vegetation, their trait variation may also be
shaped by oceanic factors including salinity and tidal currents
(Richards et al., 2021; Sousa et al., 2007). Additionally, maternal
plants can also affect propagule traits, both through genetically fixed
differences and environmentally induced transgenerational plasticity
(Alam et al., 2018; Cochrane et al., 2015). However, the relative
importance of these abiotic and biotic factors in shaping the
distribution of ITV across multiple scales, and whether the patterns
differ between the propagule function dimensions (establishment vs.
dispersal/retention) are still poorly understood.

Here we use \emph{Kandelia obovata} as the model species to investigate
the issues. \emph{Kandelia obovata} (Rhizophoraceae) is the most
cold-tolerant true-mangrove species and has a wide latitudinal
distribution along the southeast coast of China (Sheue et al., 2003;
Wang et al., 2011), providing an ideal system for studying the ITV of
mangroves. Using a stratified sampling design across a 9\selectlanguage{ngerman}° latitudinal
gradient, we analyzed the structure of intraspecific variability for
five hypocotyl functional traits in relation to propagule
dispersal/retention and establishment. The stratified sampling design
and contrasting environmental conditions allowed us to address the
following questions: (i) How is ITV structured spatially (between
populations, maternal trees, hypocotyls)? (ii) What is the major driver
(climatic, oceanic, or maternal factors) shaping the distribution of
ITV? Based on the results from previous studies (Saenger \& West, 2018;
Zhu et al., 2021), the variability of traits on the establishment axis
is expected to be higher between populations than within populations and
predominantly shaped by abiotic factors. By contrast, the trait on the
dispersal/retention axis, likely more reflecting a tradeoff between
post-disturbance recolonization and local recruitment (Sousa et al.,
2007; Van der Stocken et al., 2019), may be less structured or even
unstructured with regard to particular environmental gradients.

\textbf{2 Materials and methods}

\textbf{2.1 Species, study sites and sample collection}

\emph{Kandelia obovata} was first reported by Sheue, Liu, \& Yong
(2003), which was mainly different from the relative species \emph{K.
candel} (L.) Druce in leaf shape and cold tolerance. The two species are
well-differentiated sets of geographical populations separated by the
South China Sea. In China, \emph{K. obovata} ranges from Hainan,
Guangxi, Guangdong, Fujian, Zhejiang and Taiwan. Mature hypocotyls
of\emph{K. obovata} were collected during 2020--2021 from 26 sites along
the coastline of southern China, spanning from 19°37\selectlanguage{english}'N to 28deg41\selectlanguage{english}'N in
latitude and from 108deg05\selectlanguage{english}'E to 121deg24\selectlanguage{english}'E in longitude (Fig. 1A). This
geographic range provides wide climatic gradients in annual average
precipitation (1102--2373 mm, mean 1578 mm), annual mean temperature
(14.7--24.7 \selectlanguage{english}, mean 21.1 \selectlanguage{english}), and surface seawater salinity (13\selectlanguage{english}%0--35\selectlanguage{english}%0,
mean 21.8\selectlanguage{english}%0). For detailed site information, please refer to
Supplementary Material Table S1. We sampled 1--6 maternal trees from
each site (66 trees in total), depending on the population size. To
ensure genetic independence, the sampled trees were spaced at least 30 m
apart according to Geng et al. (2008). Each maternal tree was thus
considered as a single genealogy. We randomly collected 30 hypocotyls
from each tree for the study.

\textbf{2.2 Trait measurement forhypocotyls and maternal trees}

We measured traits including fresh weight, fresh length, maximum
transverse diameter, and minimum transverse diameter for each hypocotyl
(Fig. 1B). We also calculated the shape index of hypocotyl as the ratio
of maximum transverse diameter to the minimum (RTD). This index
describes the position of barycenter, and is thus a crucial parameter
determining the balance between propagule stranding (retention) and
buoyancy (dispersal) (Figs. 1B, 1C; Van der Stocken et al., 2019a). We
measured height (H) and diameter at breast height (DBH) for maternal
trees, and transformed them into aboveground biomass (AGB) using an
allometric model of tropical wood tree: AGB = 0.0673 x (\selectlanguage{greek}ρ \selectlanguage{ngerman}×
DBH\textsuperscript{2} × H)\textsuperscript{0.976} (Chave et al., 2014),
where wood density \selectlanguage{greek}ρ \selectlanguage{english}is set as 0.57 g/cm\textsuperscript{3} (Jiang,
2021). The aboveground biomass is a comprehensive outcome of the
interaction between the genotype and the environment experienced by
maternal trees, thereby capturing multiple facets of how maternal trees
can affect the progeny. We thus used AGB as a synthesis indicator to
predict potential effects of maternal performance on hypocotyl traits.

\textbf{2.3 Access of climate and tide data}

We extracted the data of each site from the WorldClim dataset (Hijmans
et al., 2005) for mean annual temperature (MAT) and mean annual
precipitation (MAP), and from Global Tidal Forecasting Service
(http://global-tide.nmdis.org.cn/) for tide and tidal currents datasets.
Specifically, the highest tide, the lowest tide, the fastest tidal
current and the slowest tidal current were obtained from the nearest
tide station. Since the annual average of the tide data is not
available, we used the data of four months (January, April, July and
October) to represent the whole year. We calculated the annual highest
tide (AHT), annual lowest tide (ALT), annual fastest tide current (AFC)
and annual slowest tide current (ASC). We also measured sea surface
salinity (SAL) at each site using the Portable Refractometer (Apu,
China).

\textbf{2.4 Data analysis}

Bivariate analysis with ordinary least-squares linear regression (OLR)
and quadratic regression (QR) were used to quantify how hypocotyl trait
values varied with latitudinal gradients and biotic/abiotic factors.
Because of high correlations among most hypocotyl traits (\emph{r}
\textgreater{} 0.36, \emph{P} \textless{} 0.001), we performed a
principal component analysis (PCA) with multiple traits using the
`princomp' function in R 4.1.3 (R Core Team 2022), and used the two
first PC axes to represent the hypocotyl traits.

To evaluate how environmental factors, maternal plants or inherent
factors explained variation in hypocotyl traits, we used a nested
analysis of variance (ANOVA) coupled with variance partitioning
techniques (Martin et al., 2017). We carried out linear mixed model
(LMM) for PC1 and RTD by using the `lme' function in `nlme' R package
(Pinheiro, Bates, \& R Core Team, 2022). In each model, all nested
levels (i.e. site \textgreater{} genealogy \textgreater{} within
{[}individual{]}) were entered as sequential random effects and the
intercept was the only estimated fixed effect. We then used the
`varcomp' function in the `ape' R package (Paradis et al., 2004) to
calculate the variance components associated with each nested level.

To quantify how hypocotyl traits were affected by climatic factors,
oceanic factors, and maternal performance, we implemented LMMs using the
`lme' function in the R package `nlme'. The fixed-effect terms included
the climatic, oceanic and maternal variables. To account for additional
variation potentially caused by some missing site-specific effects
(e.g., other environmental factors), and that caused by other maternal
effects uncaptured by aboveground biomass, we treated sampling site and
tree genealogy as random factors. All variables were standardised before
the modelling, such that each variable had a mean of zero and a standard
deviation of one. To reduce the adverse influence of multicollinearity,
we removed multicollinear variables until the variance inflation factors
(VIFs) of all variables in the model were less than three (Ouyang et
al., 2019). Both primary and quadratic mixed models were considered, and
only the better fitted model was showed (based on the Akike information
criteria). We calculated the VIF using the R package `car' (Fox \&
Monette, 2019). The pseudo-R\textsuperscript{2} was calculated using the
function `r.squaredGLMM' in the R package `MuMIn' (Barto\selectlanguage{polish}ń, \selectlanguage{english}2022), to
represent the variance explained by the fixed effect in the LMM. The
effect sizes of fixed factors were measured by the regression
coefficients in the LMM.

Structural equation modelling (SEM) was used to disentangle direct and
indirect effects of all predictive factors on hypocotyl traits. After
standardising all variables, multicollinear variables were removed based
on VIF. We first considered a full model that included all variables and
all reasonable pathways. Non-significant pathways were then sequentially
removed, unless the pathways were biologically informative. The removing
and adding of pathways were repeated until both\selectlanguage{greek}\emph{P\textsubscript{χ}}
\selectlanguage{english}\textsubscript{2-test} [?] 0.05 (that is, no significant difference
between model predictions and the observed data) and root mean square
error of approximation (RMSE) \textless{} 0.08 were reached (Wu et al.,
2022). The SEM was performed using the `lavaan' R package (Rosseel,
2012).

\textbf{3 Results}

\textbf{3.1 Patterns of ITV in hypocotyl along latitudinal gradients}

The first two PC axes (associated eigenvalues \textgreater{} 1)
accounted for 83.6\% of the total variation among all these traits,
where the first axis (PC1) mainly reflected hypocotyl mass and size, and
the second axis was primarily determined by the shape index (RTD; Fig.
2). PC1 (fresh weight, fresh length, maximum transverse diameter, and
minimum transverse diameter) decreased significantly with increasing
latitudinal gradients (\emph{R} \textsuperscript{2} = 0.42, \emph{P}
\textless{} 0.001). In addition, quadratic regression had a better
explanation for their relationship (\emph{R} \textsuperscript{2} = 0.53,
\emph{P}\textless{} 0.001; Fig. 3A). Intriguingly, RTD did not show any
significant correlation with latitude (\emph{P} \textgreater{} 0.05;
Fig. 3B).

\textbf{3.2 Sources of ITV in hypocotyl}

Variance partitioning showed that site was mainly contributed to
variation of PC1 (63.75\%; Fig. 4A), while variation of RTD was mainly
explained neither by site nor genealogy (69.03\% in individual; Fig.
4E).

The linear-mixed model indicated that the predictor variables explained
58\% the variation in PC1 (quadratic mixed model). Specifically,
MAT\textsuperscript{2} had the strongest negative effect on PC1 (effect
size = -0.55 \selectlanguage{ngerman}± 0.12 s. e., \emph{P} \textless{} 0.001), while MAT had a
significant positive effect on PC1 (0.38 ± 0.16, \emph{P} \textless{}
0.001). SAL had a significant negative effect on PC1 (\selectlanguage{english}-0.21 +-
0.09,\emph{P} \textless{} 0.05; Fig. 4B). Bivariate relationships
between MAT and PC1 (OLR: \emph{R} \textsuperscript{2} = 0.42;
QR:\emph{R} \textsuperscript{2} = 0.54) were much stronger than SAL and
PC1 (\emph{R} \textsuperscript{2} = 0.01; Figs. 4C, 4D). In contrast,
all these predictive factors together explained only 12\% of the
variation in the shape index, though significant effects of MAP (\selectlanguage{english}-0.30 +-
0.07) and AGB (\selectlanguage{english}-0.15 +- 0.07) were indicated by the model (Figs. 4F).
Bivariate analysis also showed that MAP and AGB had significant relation
with RTD, but the models had low explanation (\emph{R}
\textsuperscript{2} = 0.06 and\emph{R} \textsuperscript{2} = 0.02,
respectively; Figs. 4G, 4H).

\textbf{3.3 Direct and indirect effects on hypocotyl traits}

We further used SEM to disentangle the direct and indirect effects of
climatic, oceanic, and maternal factors on hypocotyl traits. MAT played
the strongest role in directly shaping PC1 (standardized path
coefficients, \selectlanguage{greek}β \selectlanguage{english}= 0.75). SAL (-0.22) was the main oceanic factors that
directly negatively affected PC1. Tide effects (ALT and AFC) were much
weaker than MAT. MAP had no direct effect and AGB had a weak effect on
PC1 (Figs. 5A, 5B). All the factors had a weak direct effect on RTD
(\textbar{}\selectlanguage{greek}β\selectlanguage{english}\textbar{} \textless{} 0.15), except for MAP (-0.31).
Compared with the effect of climatic factors on PC1, effect of climatic
factors was much smaller on RTD (Figs. 5C, 5D). Overall, these variables
can explain 64\% and 88\% of the variations in PC1 and RTD,
respectively.

\textbf{4 Discussion}

Increasing studies have showed the importance of ITV on the ecological
process. However, the patterns and sources of ITV in themselves are
still unclear (Cope et al., 2022). There are large ITVs in mangroves due
to the complex environment but are less concerned on propagule which can
determine the recruitment or/and distribution (Feller et al., 2010;
Petersan \& Bell, 2012). It is urgent to study the ITV of mangrove
propagule at the background of global changes. We analyzed the
relationship between biotic and abiotic factors and hypocotyl traits on
a large scale for a typical viviparous mangrove species. We found that
PC1 (FW, FL, TD\textsubscript{max} and TD\textsubscript{min}) was on the
establishment dimension, and ITV of PC1 was mainly structured between
populations which was directly shaped by climate. While RTD was mainly
structured between hypocotyls, which was constrained by fitness tradeoff
between dispersal and retention. Our study provides insights to the ITV
mechanism of hypocotyl traits in mangrove plants.

\textbf{4.1} \textbf{Structured traits variation of mangrove hypocotyl}

Pervious study showed that ITV in propagule size has been observed,
especially in the genera \emph{Rhizophora} , \emph{Bruguiera}
,\emph{Kandeli} and \emph{Ceriops} (Tomlinson, 2016). Our results showed
that variation of PC1 was significantly negatively correlated with
latitude, similar to the results of a previous study (Yang et al.,
2020). But there was an inverse relationship in a black mangrove study.
The propagule of \emph{Aegiceras corniculatum} was smallest in the
northern (low latitude) population, while the largest propagule was in
the southern population in Australian (Saenger \& West, 2018). The
opposite pattern of structured ITV was possibly due to the different
strategies for improving fitness between true viviparous and crypto
viviparous mangrove species. Moreover, the genetic analysis also
suggested that differences evident in populations of \emph{A.}
\emph{corniculatum} at the extremes of its range, are explained as
evolutionary adaptations in response to the local physical, climatic or
biological characteristics (Maguire et al., 2000). Therefore, ITV of PC1
sources from the local biotic and abiotic factors at different sites.

We found that climate (especially temperature) was the main driver of
intra-specific variation in PC1. In general, temperature and
precipitation have a negative relation with latitude (Hijmans et al.,
2005), while oceanic factors such as salinity and tides have little to
do with latitude. Obviously, higher temperature means that more
materials can be accumulated in hypocotyl. MAP had little effect on PC1,
probably because mangrove plants have adapted to tidal environments and
are able to obtain fresh water from seawater (Parida \& Jha, 2010).

Although oceanic factors were not found to be the main factors affecting
the variation of hypocotyl traits, salinity did have a significant
effect on PC1 variation. Salinity has long been considered an important
factor limiting the growth and distribution of mangroves (Chen \& Ye,
2014; Richards et al., 2021), and study also found that length and
weight of propagules of mangrove was negatively correlated with the
salinity (Alam et al., 2018). In addition, salt-stress is also a factor
in the evolution of vivipary in mangrove plants. The hypocotyl of
viviparous seeds contains a large amount of nutrients and water, which
are needed for the growth of newly colonized seedlings, thus avoiding
the vulnerable life stage (Joshi, 1933; Zhou et al., 2016), that also
can keep its vitality even floating in the sea for server months. When
salinity is too high, maternal tree will invest more energy to against
with salt stress (Alam et al., 2018; Parida et al., 2004), which may
reduce the investment on the hypocotyl and eventually lead to variation
in its traits. Tide is an important factor to mangrove growth and
hypocotyl dispersal process (Clarke et al., 2001; Duke et al., 1998; Van
der Stocken et al., 2015; Zhang et al., 2021), however, the effect of
tides on the variation of hypocotyl traits was marginal in our study.
The possible reason is that previous studies have only considered the
influence of tidal effects, rather than the combination of multi biotic
and abiotic effects. When count on the climate, the effect of tides may
be marginal. In addition, we suggest that PC1 is the variability of
traits on the establishment axis (Saatkamp et al., 2019; Moles \&
Westoby, 2004), which is greatly affected by resource-based factors
(such as temperature and nutrients \emph{etc.} ), while tides may not be
an important factor affecting hypocotyl mass.

\textbf{4.2} \textbf{Unstructured traits variation of mangrove
hypocotyl}

We noted that the shape index RTD did not vary with latitude, and it was
also less affected by abiotic and biotic factors. There are three
possible reasons for ITV of RTD was structured between individuals
(hypocotyls): 1) those traits may be constrained by limited genetic
variation (Bradshaw 1991). Studies have shown that the genetic diversity
of mangrove populations is low (Richards et al., 2021; Ruan et al.,
2013) and the complex matrix also limits gene flow between mangrove
populations (De Ryck et al., 2012; Van der Stocken et al., 2015); 2)
traits are related to plant fitness tradeoffs. Such as costs induce
tradeoff, they give rise to covariation between dispersal and other
life-history traits at different scales of organismal organization
(Bonte et al., 2012). Dispersal event through investments in special
morphologies, are facing the risk and opportunity for transfer and
settlement; 3) low ITV may also be caused by very sharp contrasts in
conditions in space and time (Holt \& Gomulkiewicz, 2004), which induce
the plant stress. This is exactly what happens in coastal ecosystems
where mangroves grow. Plants evolving in stressful environments have
less plasticity in making use of resources upon their increased
availability (Quadros et al., 2021). However, even the low genetic
diversity and stressful environments, there were still large ITV of
mangroves (Tomlinson, 2016), or such as the ITV of PC1. Consequently,
RTD was related to plant fitness tradeoffs.

Specifically, we inferred that RTD was correlated with
dispersal/retention tradeoff, because the center of gravity was
important to determine the position relative to the water surface while
floating (Van der Stocken et al., 2019; Figs. 1B, 1C). Although
dispersal can avoid competition with conspecific species, it also faces
the risk of colonization or/and establishment in an unfavorable
environment. On the contrary, retention means colonizing in a similar
environment to the parent tree, but cannot avoid the between conspecific
species and natural enemies (Stump \& Comita, 2020). Not all mangrove
species recolonize proportionately with the previous generation at the
same site (Kamruzzaman et al., 2017). Therefore, mangroves may regulate
the RTD to determine more dispersal or retention.\emph{K. obovata} is
the most widely distributed species of mangrove (Sheue et al., 2003;
Wang et al., 2011), the RTD of \emph{K. obovata} may be relatively small
which is facilitated to dispersal. While other Rhizophoraceae spp.,
like\emph{Bruguiera sexangular} , \emph{B. gymnorrhiza} ,
\emph{Rhizophora stylosa} \emph{etc.} are thermophilic species (Wu et
al., 2018), the RTD of those species may relatively large which can
ensure the settlement/establishment of offspring (propagule) in a
suitable place. Based on that strategy of tradeoff, mangroves can
maintain a high-level fitness.

Many studies have emphasized the importance of maternal effects in the
variation of offspring traits (Alam et al., 2018; Cochrane et al., 2015;
Galloway, 2005), but the strength of maternal effect on ITV was minor in
this study. As we only recorded the height and DBH of maternal plants,
which reflecting the size and age, that may have less to do with
morphology than with yield (Hangelbroek \& Santamaria, 2004). Rather,
heredity may be the major contributor to maternal effects. In general,
genetic diversity is low in the natural mangrove population (Hodel et
al., 2018; Richards et al., 2021), but researchers discovered that high
epigenetic diversity can maintain the high plasticity of traits in
mangrove (Mounger et al., 2021). That was possibly why the ITV of RTD
was mainly contributed from within individuals. Due to the lack of
dormant seed periods, true mangrove species do not have seed banks and
are difficult to regenerate after anthropogenic disturbances or natural
disasters (Nettel \& Dodd, 2007). Therefore, the collection of germplasm
resources and artificial cultivation of true-viviparous mangrove species
are particularly important, especially under global climate change.
Study showed that \emph{K. obovata} had higher regenerative capacity
than other viviparous species (Zhang et al., 2021), because \emph{K.
obovata} had higher dispersal capacity. We recommend that the RTD of
hypocotyl should be considered in germplasm collection and restoration,
thus conducive to the regeneration of the population. More studies
should be combined those biotic and abiotic factors to help us to have a
comprehensive understanding of the trait variation of mangroves and to
better protect the mangroves.

\textbf{Acknowledgments}

This work was supported by the Guangxi Forestry Science and Technology
Project (GLKY {[}2021{]}1). We thank Jiazhe Liu for assistance in
hypocotyl collection and determination, and Jinghang Zhou in the tide
data collection. We are also grateful for two anonymous reviewers'
helpful comments.

\textbf{Supplementary Material}

Field sites data details are reported in Supplementary Material Table
S1.

\textbf{REFERENCES}

Alam MR, Mahmood H, Khushi MLR, Rahman MM. 2018. Adaptive phenotypic
plasticity of \emph{Avicennia officinalis} L. across the salinity
gradient in the Sundarbans of Bangladesh. \emph{Hydrobiologia} 808(1):
163--174. https://doi.org/10.1007/s10750-017-3420-z

Albert CH, Thuiller W, Yoccoz NG, Soudant A, Boucher F, Saccone P,
Lavorel S. 2010. Intraspecific functional variability: extent, structure
and sources of variation. \emph{Journal of Ecology} 98(3): 604--613.
https://doi.org/10.1111/j.1365-2745.2010.01651.x

Arifanti VB, Kauffman JB, Ilman M, Tosiani A, Novita N. 2022.
Contributions of mangrove conservation and restoration to climate change
mitigation in Indonesia. \emph{Global Change Biology} 28(15):
4523--4538. https://doi.org/10.1111/10.1111/gcb.16216

Baldwin A, Egnotovich M, Ford M, Platt W. 2001. Regeneration in fringe
mangrove forests damaged by Hurricane Andrew. \emph{Plant
Ecology}157(2): 151--164. https://doi.org/10.1023/A:1013941304875

Barto\selectlanguage{polish}ń \selectlanguage{english}K. 2022. MuMIn: Multi-Model Inference. R package version 1.46.0
{[}online{]}. Available from https://CRAN.R-project.org/package=MuMIn
{[}accessed 20 August 2022{]}.

Bolnick DI, Amarasekare P, Ara\selectlanguage{ngerman}újo MS, Bürger R, Levine JM, Novak M,
Rudolf VHW, Schreiber SJ, Urban MC, Vasseur DA. 2011. Why intraspecific
trait variation matters in community ecology. \emph{Trends in Ecology
and Evolution}26(4): 183--192.
https://doi.org/10.1016/j.tree.2011.01.009

Bradshaw AD. 1991. Genostasis and the limits to
evolution.\emph{Philosophical Transactions of The Royal Society
B-Biological Sciences} 333(1267): 289--305.
https://doi.org/10.1098/rstb.1991.0079

Cardou F, Munson AD, Boisvert-Marsh L, Anand M, Arsenault A, Bell FW,
Bergeron Y, Boulangeat I, Delagrange S, Fenton NJ, Gravel D, Hamel B,
Hébert F, Johnstone JF, Kumordzi BB, Macdonald SE, Mallik A, McIntosh
ACS, McLaren JR, Messier C, Morris D, Shipley B, Sirois L, Thiffault N,
Aubin I. 2022. Above- and belowground drivers of intraspecific trait
variability across subcontinental gradients for five ubiquitous forest
plants in North America. \emph{Journal of Ecology} 110: 1590--1605.
https://doi.org/10.1111/1365-2745.13894

Chave J, Rejou-Mechain M, Burquez A, Chidumayo E, Colgan MS, Delitti WB,
Duque A, Eid T, Fearnside PM, Goodman RC, Henry M, Martínez-Yrízar A,
Mugasha WA, Muller-Landau HC, Mencuccini M, Nelson BW, Ngomanda A,
Nogueira EM, Ortiz-Malavassi E, Pélissier R, Ploton P, Ryan CM,
Saldarriaga JG, Vieilledent G. 2014. Improved allometric models to
estimate the aboveground biomass of tropical trees. \emph{Global Change
Biology} 20(10): 3177--3190. https://doi.org/10.1111/gcb.12629

Chen YP, Ye Y. 2014. Early responses of \emph{Avicennia marina} (Forsk.)
Vierh. to intertidal elevation and light level. \emph{Aquatic
Botany}112: 33--40. https://doi.org/10.1016/j.aquabot.2013.07.006

Clarke PJ, Kerrigan RA, Westphal CJ. 2001. Dispersal potential and early
growth in 14 tropical mangroves: do early life history traits correlate
with patterns of adult distribution? \emph{Journal of Ecology} 89(4):
648--659. https://doi.org/10.1046/j.0022-0477.2001.00584.x

Cochrane A, Yates CJ, Hoyle GL, Nicotra AB. 2015. Will among-population
variation in seed traits improve the chance of species persistence under
climate change? \emph{Global Ecology and Biogeography} 24(1): 12--24.
https://doi.org/10.1111/geb.12234

Cope OL, Burkle LA, Croy JR, Mooney KA, Yang LH, Wetzel WC. 2022. The
role of timing in intraspecific trait ecology. \emph{Trends in Ecology
and Evolution} 37(11): 997--1005. https://doi.org/
10.1016/j.tree.2022.07.003

De Ryck DJR, Robert EMR, Schmitz N, Van der Stocken T, Di Nitto D,
Dahdouh-Guebas F, Koedam N. 2012. Size does matter, but not only size:
Two alternative dispersal strategies for viviparous mangrove
propagules.\emph{Aquatic Botany} 103: 66--73.
https://doi.org/10.1016/j.aquabot.2012.06.005

Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M, Kanninen
M. 2011. Mangroves among the most carbon-rich forests in the
tropics.\emph{Nature Geoscience} 4(5): 293--297.
https://doi.org/10.1038/Ngeo1123

Duke NC, Ball MC, Ellison JC. 1998. Factors influencing biodiversity and
distributional gradients in mangroves. \emph{Global Ecology and
Biogeography} 7(1): 27--47. https://doi.org/ 10.2307/2997695

Duke NC, Meynecke JO, Dittmann S, Ellison AM, Anger K, Berger U,
Cannicci S, Diele K, Ewel KC, Field CD, Koedam N, Lee SY, Marchand C,
Nordhaus I, Dahdouh-Guebas F. 2007. A world without
mangroves?\emph{Science} 317(5834): 41--42.
https://doi.org/10.1126/science.317.5834.41b

Feller IC, Lovelock CE, Berger U, McKee KL, Joye SB, Ball MC. 2010.
Biocomplexity in Mangrove Ecosystems. \emph{Annual Review of Marine
Science} 2: 395--417.
https://doi.org/10.1146/annurev.marine.010908.163809

Fox J, Monette G. 2019. An \{R\} Companion to Applied Regression, Third
Edition. Thousand Oaks CA: Sage {[}online{]}. Available from
https://socialsciences.mcmaster.ca/jfox/Books/Companion/ {[}accessed 20
August 2022{]}.

Friess DA, Rogers K, Lovelock CE, Krauss KW, Hamilton SE, Lee SY, Lucas
R, Primavera J, Rajkaran A, Shi S. 2019. The state of the world's
mangrove forests: past, present, and future. \emph{Annual Review of
Environment and Resources} 44(1): 89--115.
https://doi.org/10.1146/annurev-environ-101718-033302

Galloway LF. 2005. Maternal effects provide phenotypic adaptation to
local environmental conditions. \emph{New Phytologist} 166(1): 93--99.
https://doi.org/10.1111/j.1469-8137.2004.01314.x

Geng QF, Lian CL, Goto S, Tao JM, Kimura M, Islam MDS, Hogetsu T. 2008.
Mating system, pollen and propagule dispersal, and spatial genetic
structure in a high-density population of the mangrove
tree\emph{Kandelia candel} . \emph{Molecular Ecology} 17(21):
4724--4739. https://doi.org/10.1111/j.1365-294X.2008.03948.x

Giri C, Long J, Abbas S, Murali RM, Qamer FM, Pengra B, Thau D. 2015.
Distribution and dynamics of mangrove forests of South
Asia.\emph{Journal of Environmental Management} 148: 101--111.
https://doi.org/10.1016/j.jenvman.2014.01.020

Grubb PJ. 1977. Maintenance of species-richness in plant communities:
the importance of the regeneration niche. \emph{Biological Reviews of
the Cambridge Philosophical Society} 52: 107--145.

Hangelbroek HH, Santamaria L. 2004. Regulation of propagule size in the
aquatic pseudo-annual \emph{Potamogeton pectinatus} : are genetic and
maternal non-genetic effects additive? \emph{Evolutionary Ecology
Research} 6(1): 147--161.

Hijmans RJ., Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high
resolution interpolated climate surfaces for global land
areas.\emph{International Journal of Climatology} 25(15): 1965--1978.
https://doi.org/10.1002/joc.1276

Hodel RGJ, Knowles LL, McDaniel SF, Payton AC, Dunaway JF, Soltis PS,
Soltis DE. 2018. Terrestrial species adapted to sea dispersal:
Differences in propagule dispersal of two Caribbean
mangroves.\emph{Molecular Ecology} 27(22): 4612--4626.
https://doi.org/10.1111/mec.14894

Holt RD, Gomulkiewicz R. 2004. \emph{Conservation implication of niche
conservatism and evolution in heterogeneous environments} .
In:\emph{Evolutionary Conservation Biology} . UK: Cambridge University
Press. 13: 244--264.

Jiang X. 2014. \emph{Xylem hydraulic structure and function in
mangroves} . Ph.D. Dissertation. Guangxi: Guangxi University.

Joshi AC. 1933. A suggested explanation of the prevalence of vivipary on
the sea-shore. \emph{Journal of Ecology} 21(1): 209--212.

Kamruzzaman M, Minhaj-Uj-Siraj M, Ahmed S, Osawa A. 2017. Regeneration
status of mangrove species under mature stands in the oligohaline zone
of the Sundarbans, Bangladesh. \emph{Regional Studies in Marine
Science}16: 15--20. https://doi.org/10.1016/j.rsma.2017.07.007

Kendall BE, Fox GA. 2003. Unstructured individual variation and
demographic stochasticity. \emph{Conservation biology} 17(4):
1170--1172. https://doi.org/10.1046/j.1523-1739.2003.02411.x

Krauss KW, Lovelock CE, Mckee KL, Lopez-Hoffman L, Ewe SML, Sousa WP.
2008. Environmental drivers in mangrove establishment and early
development: A review. \emph{Aquatic Botany} 89(2): 105--127.
https://doi.org/10.1016/j.aquabot.2007.12.014

Kumordzi BB, Aubin I, Cardou F, Shipley B, Violle C, Johnstone J, Anand
M, Arsenault A, Bell FW, Bergeron Y, Boulangeat I, Brousseau M, Grandpré
LD, Delagrange S, Fenton NJ, Gravel D, Macdonald SE, Hamel B, Higelin M,
Hébert F, Isabel N, Mallik A, McIntosh ACS, McLaren JR, Messier C,
Morris D, Thiffault N, Tremblay JP, Munson AD. 2019. Geographic scale
and disturbance influence intraspecific trait variability in leaves and
roots of North American understorey plants. \emph{Functional
Ecology}33(9): 1771--1784. https://doi.org/10.1111/1365-2435.13402

Martin AR, Rapidel B, Roupsard O, Van den Meersche, K., de Melo Virginio
Filho, E., Barrios, M., Isaac, M.E., 2017. Intraspecific trait variation
across multiple scales: the leaf economics spectrum in
coffee.\emph{Functional Ecology} 31(3): 604--612.
https://doi.org/10.1111/1365-2435.12790

Mckee KL. 1995. Mangrove species distribution and propagule predation in
Belize: an exception to the dominance-predation
hypothesis.\emph{Biotropica} 27(3): 334---345.

Moles AT, Westoby M. 2004. Seedling survival and seed size: a synthesis
of the literature. \emph{Journal of Ecology} 92(3): 372--383.
https://doi.org/10.1111/j.0022-0477.2004.00884.x

Moran EV, Hartig F, Bell DM. 2016. Intraspecific trait variation across
scales: implications for understanding global change
responses.\emph{Global Change Biology} 22(1): 137--150.
https://doi.org/10.1111/gcb.13000

Mounger J, Boquete MT, Schmid MW, Granado R, Robertson MH, Voors SA,
Langanke KL, Alvarez M, Wagemaker CAM, Schrey AW, Fox GA, Lewis DB, Lira
CF, Richards CL. 2021. Inheritance of DNA methylation differences in the
mangrove \emph{Rhizophora mangle} . \emph{Evolution and
Development}23(4): 351--374. https://doi.org/10.1111/ede.12388

Nettel A, Dodd RS. 2007. Drifting propagules and receding swamps:
genetic footprints of mangrove recolonization and dispersal along
tropical coasts. \emph{Evolution} 61(4): 958--971.
https://doi.org/10.1111/j.1558-5646.2007.00070.x

Ouyang S, Xiang WH, Wang XP, Xiao WF, Chen L, Li SG, Sun H, Deng XW,
Forrester DI, Zeng LX, Lei PF, Lei XD, Gou MM, Peng CH. 2019. Effects of
stand age, richness and density on productivity in subtropical forests
in China. \emph{Journal of Ecology} 107(5): 2266--2277.
https://doi.org/10.1111/1365-2745.13194

Paradis E, Claude J, Strimmer K. 2004. APE: analyses of phylogenetics
and evolution in R language. \emph{Bioinformatics} 20(2): 289--290.
https://doi.org/10.1093/bioinformatics/btg412

Parida AK, Jha B. 2010. Salt tolerance mechanisms in mangroves: a
review. \emph{Trees-Structure and Function} 24(2): 199--217.
https://doi.org/10.1007/s00468-010-0417-x

Parida AK, Das AB, Sanada Y, Mohanty P. 2004. Effects of salinity on
biochemical components of the mangrove, \emph{Aegiceras corniculatum}
.\emph{Aquatic Ecology} 80(2): 77--87.
https://doi.org/10.1016/j.aquabot.2004.07.005

Peterson JM, Bell SS. 2012. Tidal events and salt-marsh structure
influence black mangrove (\emph{Avicennia germinans} ) recruitment
across an ecotone. \emph{Ecology} 93(7): 1648--1658.
https://doi.org/10.1890/11-1430.1

Pinheiro J, Bates D, R Core Team. 2022. nlme: Linear and Nonlinear Mixed
Effects Models. R package version 3.1--158 {[}online{]}. Available from
https://CRAN.R-project.org/package=nlme {[}accessed 20 August 2022{]}.

Proffitt CE, Travis S. 2014. Red mangrove life history variables along
latitudinal and anthropogenic stress gradients. \emph{Ecology and
Evolution} 4(12): 2352--2359. https://doi.org/10.1002/ece3.1095

R Core Team, 2022. R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
Available from https://www.R-project.org/ {[}accessed 20 August 2022{]}.

Rabinowitz D. 1978. Dispersal properties of mangrove
propagules.\emph{Biotropica} 10(1), 47--57.
https://doi.org/10.2307/2388105

Richards CL, Langanke KL, Mounger J, Fox GA, Lewis DB. 2021. Trait
Response to Nitrogen and Salinity in\emph{Rhizophora} \emph{mangle}
Propagules and Variation by Maternal Family and Population of Origin.
\emph{Frontiers in Marine Science} 8.
https://doi.org/10.3389/fmars.2021.756683

Richards CL, Pennings SC, Donovan LA. 2005. Habitat range and phenotypic
variation in salt marsh plants. \emph{Plant Ecology} 176(2): 263--273.
https://doi.org/ 10.1007/s11258-004-0841-3

Rosseel Y. 2012. lavaan: An R Package for Structural Equation
Modeling.\emph{Journal of Statistical Software} 48(2): 1--36.
https://doi.org/10.18637/jss.v048.i02

Ruan Y, Huang BH, Lai SJ, Wan YT, Li JQ, Huang S, Liao PC. 2013.
Population genetic structure, local adaptation, and conservation
genetics of \emph{Kandelia obovata} . \emph{Tree Genetics and
Genomes}9(4): 913--925. https://doi.org/10.1007/s11295-013-0605-0

Saatkamp A, Cochrane A, Commander L, Guja LK, Jimenez-Alfaro B, Larson
J, Nicotra A, Poschlod P, Silveira FAO, Cross AT, Dalziell EL, Dickie J,
Erickson TE, Fidelis A, Fuchs A, Golos PJ, Hope M, Lewandrowski W,
Merritt DJ, Miller BP, Miller RG, Offord CA, Ooi MKJ, Satyanti A,
Sommerville KD, Tangney R, Tomlinson S, Turner S, Walck JL. 2019. A
research agenda for seed-trait functional ecology. \emph{New
Phytologist} 221(4): 1764--1775. https://doi.org/10.1111/nph.15502

Saenger P, West PW. 2018. Phenotypic variation of the mangrove
species\emph{Avicennia marina} (Forssk.) Vierh. from seven provenances
around Australia. \emph{Aquatic Ecology} 149: 28--32.
https://doi.org/10.1016/j.aquabot.2018.05.004

Sheue CR, Liu HY, Yong JWH. 2003. \emph{Kandelia
obovata}(Rhizophoraceae), a new mangrove species from Eastern Asia.
\emph{Taxon}52(2): 287--294. https://doi.org/10.2307/3647398

Sousa WP, Kennedy PG, Mitchell BJ, Ordóñez L BM. 2007. Supply\selectlanguage{english}-side
ecology in mangroves: do propagule dispersal and seedling establishment
explain forest structure? \emph{Ecological Monographs} 77(1): 53--76.
https://doi.org/10.1890/05-1935

Stump SM, Comita LS. 2020. Differences among species in seed dispersal
and conspecific neighbor effects can interact to influence
coexistence.\emph{Theoretical Ecology} 13(4): 551--581.
https://doi.org/10.1007/s12080-020-00468-5

Tomlinson P. 2016. \emph{The Botany of Mangroves} (2nd ed.). Cambridge:
Cambridge University Press. doi:10.1017/CBO9781139946575

Van der Stocken T, De Ryck DJR, Vanschoenwinkel B, Deboelpaep E, Bouma
TJ, Dahdouh-Guebas F, Koedam N. 2015. Impact of landscape structure on
propagule dispersal in mangrove forests. \emph{Marine Ecology Progress
Series} 524: 95--106. https://doi.org/10.3354/meps11206

Van der Stocken T, Wee AKS, De Ryck DJR, Vanschoenwinkel B, Friess DA,
Dahdouh-Guebas F, Simard M, Koedam N, Webb EL. 2019. A general framework
for propagule dispersal in mangroves. \emph{Biological Reviews} 94(4):
1547--1575. https://doi.org/10.1111/brv.12514

Violle C, Enquist BJ, McGill BJ, Jiang L, Albert CH, Hulshof C, Jung V,
Messier J. 2012. The return of the variance: intraspecific variability
in community ecology. \emph{Trends in ecology and evolution} 27(4):
244--252. https://doi.org/10.1016/j.tree.2011.11.014

Wang WQ, You SY, Wang YB, Huang L, Wang M. 2011. Influence of frost on
nutrient resorption during leaf senescence in a mangrove at its
latitudinal limit of distribution. \emph{Plant and Soil} 342(1--2):
105--115. https://doi.org/10.1007/s11104-010-0672-z

Wu LW, Zhang Y, Guo X, Ning DL, Zhou XS, Feng JJ, Yuan MM, Liu S, Guo
JJ, Gao, ZP, Ma J, Kuang JL, Jian SY, Han S, Yang ZF, Ouyang Y, Fu Y,
Xiao NJ, Liu XD, Wu LY, Zhou AF, Yang YF, Tiedje JM, Zhou JZ. 2022.
Reduction of microbial diversity in grassland soil is driven by
long-term climate warming. \emph{Nature Microbiology} 7: 1054--1062.
https://doi.org/10.1038/s41564-022-01147-3

Wu YT, Ricklefs RE, Huang ZJ, Zan QJ, Yu SX. 2018. Winter temperature
structures mangrove species distributions and assemblage composition in
China. \emph{Global Ecology and Biogeography} 27(12): 1492--1506.
https://doi.org/10.1111/geb.12826

Yang S, Liu X, Deng RJ, Chen QX, Wang JW, Lu X. 2020. Geographic
variations of hypocotyl and seedling growth traits for \emph{Kandelia
obovata} with different provenances. \emph{Chinese Journal of
Ecology}39(6): 1769--1777. https://doi.org/10. 13292 /j. 1000--4890.
202006. 003

Zhang Y, Xin K, Sheng N, Xie ZL, Liao BW. 2021. The regenerative
capacity of eight mangrove species based on propagule traits in Dongzhai
Harbor, Hainan Province, China. \emph{Global Ecology and
Conservation}30: e01788. https://doi.org/ 10.1016/j.gecco.2021.e01788

Zhu H, Lin HJ, Yang L, Li HP, Yue CL, Jiang B. 2021. Geographical
distribution pattern and environmental explanation of \emph{Kandelia
obovata} Sheue, H. Y. Liu \& J. Yong populations along the Southeast
coast of China. \emph{Plant Science Journal} 39(5): 476--487.
https://doi.org/10.11913/PSJ. 2095-0837. 2021. 50476

\textbf{Figure Legends}

\textbf{Fig. 1.} Distribution of sampling sites and hypothesis of
hypocotyl dispersal status. \textbf{A,} Sampling sites of \emph{Kandelia
obovata} hypocotyl on the south coast of China. The color gradient
indicates the salinity (\selectlanguage{english}%0) of surface seawater. Circle size is
proportional to genealogies size (N). See Table S1 for a detailed
description of field sites. \textbf{B,} Determination of hypocotyl
traits: length, maximum transverse diameter (TD\textsubscript{max}) and
minimum transverse diameter (TD\textsubscript{min}). \textbf{C,} The
hypothesis of different ratio of TD\textsubscript{max} to
TD\textsubscript{min} (RTD) of hypocotyl have different positions
relative to the water surface while floating and illustration of the
linkage of RTD and tradeoff between dispersal and retention.

\textbf{Fig. 2.} Principal component analysis (PCA) of hypocotyl traits.
FW, fresh weight; FL, fresh length; TD\textsubscript{max}, maximum
transverse diameter; TD\textsubscript{min}, minimum transverse diameter;
RTD, ratio of TD\textsubscript{max} to TD\textsubscript{min}.

\textbf{Fig. 3.} Patterns of hypocotyl intraspecies traits variations
(ITV) along latitudinal gradients.\textbf{A,} Relationship between PC1
and latitude. \textbf{B,} Relationship between RTD and latitude. Bule
line is ordinary least-squares linear regression (OLR), and red line is
quadratic regression (QR). Solid line indicates statistical significance
(\emph{P} \textless{} 0.05), dashed line indicates statistical
non-significance (\emph{P}\textgreater{} 0.05). Filled area means SE.
PC1, the first principal component. RTD, ratio of TD\textsubscript{max}
to TD\textsubscript{min}.

\textbf{Fig. 4.} Sources of hypocotyl intraspecies traits variations
(ITV). \textbf{A, E,} Variance partitioning of PC1 and RTD across three
nested levels of organization.\textbf{B, F,} Summary of the linear
mixed-effect modeling for multiple biotic and abiotic factors on PC1 and
RTD. Data are presented as coefficients +- standard errors of the
estimated effect sizes.\emph{R} \textsuperscript{2}m represents
marginal\emph{R} \textsuperscript{2}, \emph{R} \textsuperscript{2}c
represents conditional \emph{R} \textsuperscript{2}. *, \emph{P}
\textless{} 0.05; ***, \emph{P} \textless{} 0.001. \textbf{C, D, G, H,}
The relationships between significant biotic and abiotic factors and PC1
and RTD. Bule line is ordinary least-squares linear regression (OLR),
and red line is quadratic regression (QR). Filled area means SE. SAL,
sea surface salinity; MAT, mean annual temperature; MAP, mean annual
precipitation; AFC, annual fastest tide current; ALT, annual lowest
tide; AGB, aboveground biomass. PC1, the first principal component. RTD,
ratio of TD\textsubscript{max} to TD\textsubscript{min}.

\textbf{Fig. 5.} Direct and indirect effects of biotic and abiotic
factors on hypocotyl intraspecies traits variations (ITV). \textbf{A,
C,} Structural equation models (SEMs) showing the relationships among
biotic, abiotic factors and PC1 and RTD.\textbf{B, D,} Standardized
effects derived from SEMs of PC1 and RTD. Red and black arrows indicate
positive and negative relationships, respectively. Solid or dashed lines
indicate significant (\emph{P}\textless{}\selectlanguage{english} 0.05) or non-significant
relationships. Numbers near the pathway arrow indicate the standard path
coefficients.\emph{R} \textsuperscript{2} represents the proportion of
variance explained. The relevant indicators are abbreviated as: PC1, the
first principal component; RTD, ratio of TD\textsubscript{max} to
TD\textsubscript{min}; AGB, aboveground biomass; SAL, sea surface
salinity; AFC, annual fastest tide current; ALT, annual lowest tide;
MAT, mean annual temperature; MAP, mean annual precipitation.

\par\null

\textbf{Figure 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 4}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 5}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

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

