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Soil nutrients and leaf area index interact with species
and structural diversity to buffer mangrove productivity
against salinity
Shamim Ahmed1,2,*, Swapan Kumar Sarker3, Md. Kamruzzaman2, Saverio Perri4,5, Torben Hilmers1,
Enno Uhl1, Md. Rifat Hossain3, Nazifa Tasnim3, Clement Sullibie Saagulo Naabeh6,
Tabia Tasnim Anika3, Md Mizanur Rahman7, Hans Pretzsch1
1 Tree Growth and Wood Physiology, Department of Life Science Systems, School of Life Sciences, Technical University of Munich,
Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
2 Forestry and Wood Technology Discipline, Khulna University, Khulna 9208, Bangladesh
3 Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, Bangladesh
4 Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA
5 High Meadows Environmental Institute, Princeton University, Guyot Hall, Princeton, NJ 08544, USA
6 Institute of Environment and Sanitation Studies, University of Ghana, International Programmes Office, MR39+C4X, Annie Jiagge Rd, Accra, Ghana
7 Jiangmen Laboratory of Carbon Science and Technology, Hong Kong University of Science and Technology (Guangzhou), Jiangmen 529199, China
* Corresponding author. E-mail: shamim.ahmed@tum.de (S. Ahmed)
Received October 2, 2024; Revised December 26, 2024; Accepted January 21, 2025
©The Author(s) 2025. This article is published with open access at link.springer.com and journal.hep.com.cn
ABSTRACT
● The comparative roles of species and
structural diversity in mitigating the
impacts of salinity were evaluated.
● Greater diversity contributes to miti-
gating salinity impacts by interacting
with nutrients and leaf functional trait.
● Nutrients and leaf functional trait (leaf
area) significantly influenced the effects
of salinity on mangrove growth.
● Future growth models should incor-
porate functional traits and nutrient
availability to improve predictions of mangrove growth under saline conditions.
Mangroves show a biogenic response to adjust sea-level rise by accumulating sediment and carbon (vertical soil accretion), reshaping their
structure and composition to minimize the effects. Additionally, the often-overlooked factors of soil nutrient availability, functional traits, and stand
structure can alter the mangrove diversity-salinity-productivity link. However, how these multiple drivers interplay to maintain growth against
salinity still needs to be better understood. Considering all these, we answered two questions: (QI) How do species diversity and structural
heterogeneity modulate growth vs. salinity relationships? (QII) To what extent can structural heterogeneity and species diversity create optimal
conditions by minimizing the adverse effects of salinity while concurrently maximizing forest growth? To comprehensively understand the interplay
between structural and species diversity, nutrient availability, functional traits, and rising salinity, we examined a dataset from 60 permanent plots
established in the Sundarbans mangrove forest in Bangladesh. Our results indicated that species diversity less directly contributed to forest
growth than structural heterogeneity, nutrient availability (N, P, and K), and leaf area index. While forest structural and species diversity alone is
unlikely to optimize growth, incorporating nutrients into the models showed a slight improvement in buffering against salinity. However, when
nutrients were combined with the leaf area index, the models indicated a much stronger enhancement in the forest’s resilience to salinity through
interactions with these factors, allowing continued growth. In conclusion, our study highlights the relative contributions of species and structural
diversity to mangrove growth under stress and the potential roles of nutrients and functional traits. These findings are valuable for forest growth
modelling, informing conservation and management strategies for mangroves, particularly in coastal plantations facing environmental changes.
Keywordsmangrove productivity, structure-functions relation, competition, density, sediment nutrients, functional traits, climate change
Cite this: Soil Ecol. Lett., 2025, 7(2): 250299
RESEARCH ARTICLE
Volume 7 / Issue 2 / 250299 / 2025
https://doi.org/10.1007/s42832-025-0299-x
1Introduction
Mangrove forests, situated at the intersection of terrestrial
and marine environments, hold a significant role in coastal
ecosystems (Alongi, 2008). They offer many ecosystem
services, including livelihoods to millions of coastal people,
shoreline stabilization, protection of coastal communities
from tidal surges and cyclones, habitats for various
species, and sequestrate huge amounts of carbon per year
(210 Tg C yr−1) (Donato et al., 2011; Alongi, 2012, 2014;
Yoshikai et al., 2021; Ahmed et al., 2023). However, these
vital ecosystems are facing unprecedented challenges due
to the impacts of climate change, in particular, the rising
level of sea level and salinity and shifts in hydrological
patterns (Lovelock et al., 2015; Singh, 2020). Global sea
level rise, attributed to climate change, has resulted in
extensive changes to mangrove habitats and coastal alter-
ations (Sofawi et al., 2017). This phenomenon not only
threatens water availability necessary for mangrove survival
but also triggers complex ecological responses that intertwine
with other intrinsic factors governing the growth of mangrove
forests by reducing structure and changing species compo-
sition, which is now well recognized in the Indo-pacific
regions (Lovelock et al., 2015; Yoshikai et al., 2021; Perri
et al., 2023). Furthermore, the intensity of salinity is
projected to increase and is identified as one of the most
critical factors that may retard mangrove functional and
physiological activities by reducing seed germination (Mitra
et al., 2010), leaf size and shape (Mollick et al., 2021), fine
root production (Ahmed et al., 2021), tree height (Perri et al.,
2023), wood density (Rahman et al., 2021a), and site quality
(Ahmed et al., 2022), for example. In response to climate
change-induced sea level rise, mangroves adopt a biogenic
approach by accumulating organic carbon and sediment
(Saintilan et al., 2023). Although few studies explain species
diversity through facilitation may help to reduce the salinity
impacts (Huxham et al., 2010). However, less is understood
regarding the biogenic response of mangroves to salinity,
mainly how this response may involve changes in structural
and species diversity combined with nutrients and functional
traits for maintaining growth.
Biodiversity-productivity relationship is often observed in
both tropical and temperate forests, as demonstrated by
various studies (Vanelslander et al., 2009; Williams et al.,
2017; Ali et al., 2019; Park et al., 2019; Zheng et al., 2019).
The finding of this relationship plays a critical role in under-
standing the growth patterns against stress as tree produc-
tivity is mainly dependent on the structural and species
diversity (Ahmed et al., 2023; Astigarraga et al., 2023;
Pretzsch et al., 2023), while other driver effects can be
manifested into accumulated tree structure. Forest stands
with higher species and structural diversity facilitating each
other through complementarity (Huxham et al., 2010; Jucker
et al., 2015). Complementarity occurs when different
species occupy different ecological niches, enabling efficient
resource utilization by plants and increasing aboveground
biomass (Paquette and Messier, 2011; Forrester et al.,
2013; Fotis et al., 2018). This is achieved by reducing inter-
specific competition between species via stratification in
crown and root areas (Riofrío et al., 2017; Condés et al.,
2023). Several studies reported that structural diversity is
more important than species diversity to maintain above-
ground biomass growth (Ali et al., 2016; Dănescu et al.,
2016; Park et al., 2019; Astigarraga et al., 2023), as species
diversity impact can be mediated by structure (Park et al.,
2019). Structural diversity is considered a reliable ecosystem
function predictor (LaRue et al., 2019, 2023a, 2023b). In
contrast, with the increasing of studies on the association of
species diversity with forest growth in different locations
and/or biomes, conflicting findings emerged-positive, nega-
tive, or even neutral (Cavard et al., 2010; Del Río et al.,
2022), which initiates dispute among scientists (Dănescu
et al., 2016). This debate is likely to occur due to inappropriate
consideration of tree age, nutrients, stand density, and
different aspects of biodiversity because these driving
factors can define the structure and growth from the individual
tree to the stand level under stress (Pretzsch, 2009; Condés
et al., 2013; Ali et al., 2016; Schmied et al., 2023). In parti-
cular, when stand density is a critical factor that may
increase competition and reduce growth (Pretzsch, 2005;
Ng et al., 2016). When stand density is so high that light
becomes a limiting factor, the significance of the treeʼs
spatial arrangement in defining interspecific competition,
relationships, and other competitive events may diminish
(Amoroso and Turnblom, 2006). A recent experimental
study has reported that constant tree growth in mixed forest
stands under low to medium tree density (Thurm and
Pretzsch, 2021).
Understanding the intricate dynamics influencing mang-
rove growth amid changing environmental conditions poses
a significant challenge. The delicate interplay between rising
salinity and density-induced competition is further compli-
cated by the crucial role of nutrients—nitrogen (N), phospho-
rus (P), and potassium (K), with nitrogen identified as the
primary growth-limiting factor for mangroves (Alongi, 2020).
As salinity levels increase, nutrient availability tends to
decrease, potentially leading to decreased site conditions
and productivity (Ahmed et al., 2022), as productivity is
strongly correlated to site conditions (Sun et al., 2017). To
adjust the site conditions, plants modify the functional traits
(Yaseen et al., 2023) and increase the functional trait vari-
ability (Price et al., 2014). Later, the modified functional
traits will likely modulate plant dynamics (Pérez-Ramos
2 Mangroves buffers against salinity
et al., 2019). Despite the widespread acknowledgment of
the adverse effects of increased salinity, the influence of
diversity in associations with nutrients and functional traits
on growth in mangroves remains a largely unexplored
aspect. Notably, the role of density-modified structure and
the buffering potential of nutrient availability among
mangrove trees have received limited attention, even though
they hold paramount importance in scientific research and
management considerations. Adding to the complexity, the
relative extent to which structural and species diversity act
as buffers against salinity to optimize growth remains largely
unexplored. Bridging these gaps in our understanding is
crucial for comprehensive insights into mangrove ecosystems
under changing conditions.
Several studies on a regional to global scale have demon-
strated that species diversity, tree density management, and
thinning can increase forest growth by lowering competition
for resources like light, water, and nutrients (Miller, 1997;
Zhang et al., 2012; LaManna et al., 2017; Güney et al.,
2022; Qu et al., 2022). If forest growth needs to be
sustained for a long time, management strategies must be
modified in response to the effects of climate change (Millar
et al., 2007). Long-term growth can be maximized by
employing adaptive forest management techniques, including
monitoring and modifying forest structure in response to
changing conditions (Lindenmayer et al., 2011). By integrat-
ing these principles and practices into forest management,
forest ecosystems’ ecological health and resilience can be
preserved while maximizing growth. Consequently, forest
regions’ future composition and growth will depend on how
tree species respond to projected climate change and local
environmental conditions. Despite the strong impact of
species and structural diversity on forest functions in climate
change scenarios, understanding their comparative role
remains contentious.
After considering the above discussion, the overarching
objective of this study is to gain a comprehensive under-
standing of the multifaceted interactions among structural
diversity, species diversity, nutrient availability, density-
induced competition, and rising salinity and how these inter-
actions collectively or individually shape the growth patterns
of mangrove forests (Fig. 1). To do this, we formulated two
specific research questions and hypotheses.
(QI) How do species diversity and structural heterogeneity
influence growth-salinity relationships? We hypothesized
that (HI): Structural and species diversity has a buffering
role in mitigating salinity impacts by interacting with nutrient
availability and functional traits.
Moreover, (QII): To what extent does the structural hetero-
geneity and species diversity contribute to buffer forest
conditions by minimizing the adverse effects of salinity while
concurrently maximizing forest growth potential? To evaluate
whether structural and species diversity can optimize growth
by reducing salinity impacts, we hypothesized (HII) that the
combined influence of structural heterogeneity, species
diversity, nutrients, and functional traits would optimize
growth. However, we also hypothesized that nutrients and
functional characteristics might play a crucial role in enhanc-
ing the model’s capability and potentially help to reduce
salinity impacts.
To address our research questions, we employed a
dataset from the Sundarbans Reserve mangrove Forest
(SRF) of Bangladesh, one of the largest and most diverse
mangrove forests in the world (Rahman et al., 2015; Islam
et al., 2016). This dataset includes information on stand
structural and compositional diversity, salinity gradients, and
Fig.1 Conceptual framework illustrating the influence of salinity on forest parameters and growth buffering mechanisms.
(A) The variability of forest parameters across salinity levels and (B) the interaction of salinity and structural diversity, species
diversity (proportional weight), and nutrients used in this study to describe growth differences and responses. The conceptual
diagram explains that salinity stress actively reduces tree growth by limiting structure (tree height, H; diameter at breast height,
DBH) and inhibiting resources (nutrients) and leaf functions (leaf area index, LAI). When the forest is structurally diverse in
species and structure and resources are abundant, the stress impacts are buffered and promote growth. In panel A, darker soil
color indicates higher availability of nutrients (N, P, and K).
Shamim Ahmed et al. 3
nutrients (N, P, and K). We chose this dataset because of
the ecological significance of the Sundarbans, which encom-
passes a large area and is located in an active delta
(Bangladesh part: 6 017 km2). The SRF exhibits salinity
gradients species diversity and plays a significant role in
mitigating climate change through sequestering and storing
atmospheric carbon and addressing natural calamities like
tropical cyclones (Rogers and Goodbred, 2014; Akber et al.,
2018; Sarker et al., 2019a, 2019b; Ahmed et al., 2022). This
region, in turn, holds the potential to inform evidence-based
strategies for conserving and managing these crucial
coastal ecosystems in the face of dynamic global changes.
By enhancing our understanding of the intricate interactions
between ecological variables, this research could contribute
to scientific knowledge and offer practical guidance for safe-
guarding endangered mangrove ecosystems from future
threats and degradation.
2Materialsandmethods
2.1 Study site description and tree inventory
This research was carried out within the unique ecosystem
of the Sundarbans Reserve Mangrove Forest (SRF) in
Bangladesh, as illustrated in Fig. S1. SRF is located in an
active delta (Rogers and Goodbred, 2014) and faces
increasing salinity challenges (Lovelock et al., 2015; Ahmed
et al., 2022). Due to increasing salinity, SRF loses productive
species or less salinity-tolerant species (Sarker et al.,
2019a), which makes SRF less productive in high-salinity
areas by restricting tree height growth (Ahmed et al., 2022;
Perri et al., 2023). SRF has been classified as three saline
zones based on river water salinity levels, namely oligohaline
(<14 ppt), mesohaline (14–25 ppt), and polyhaline (>25 ppt)
(Ahmed et al., 2022). To comprehensively assess the
forest’s structure, species composition, and carbon storage,
we established 60 permanent sample plots (PSPs). Each of
these plots covered an area of 0.01 hectares. Our sampling
strategy involved the creation of 20 PSPs within each of the
three salinity eco-regions. This allocation was achieved
through a stratified random sampling approach, ensuring
representation across the salinity zones. This first fieldwork
was conducted in April 2018.
To document the tree diversity, we identified and labeled
all trees with a minimum diameter at breast height (DBH) of
4.6 cm, measured at 1.3 meters from the ground. Aluminum
tags were affixed to these trees for identification purposes.
Concurrently, tree heights were measured utilizing an elec-
trical Dendrometer, specifically the Criterion RD 1 000 model
from Laser Technology Incorporation in the USA. Adopting
the 4.6 cm DBH criterion is in line with historical practice,
dating back to the 1900s, and is particularly suited to
mangroves given their relatively slow aboveground growth
pattern.
In November 2020, we revisited all 60 PSPs to remeasure
and assess the biomass growth patterns of the tagged
trees—data collection involved measuring the DBH and
heights of all the 1 378 trees tagged in 2018. However, we
excluded all dead trees during the growth (aboveground
biomass changes) calculation.
2.2 Stand structure, species composition, nutrients, and
biomass estimation
We utilized various tree measurements to assess the char-
acteristics of the forest stand (Table 1). These measurements
included stand density (number of stems per hectare), mean
Table1 List of plot-level variables used in this study.
Variables and metrics names Abbreviated form Unit/calculation
Mean diameter at breast height DBH cm
Horizontal diversity (coefficient of variation of DBH) cv of DBH variation of DBH (cv of DBH=sd DBH/mean DBH)
Mean tree height H m
Vertical diversity (coefficient of variation of H) cv of H variation of H (cv of H=sd H/mean H)
Initial stand density Density stems ha‒1
Aboveground biomass stocks AGB mg ha‒1 (biomass equation adopted from
Ahmed et al. (2022))
Aboveground biomass changes over time AGB growth or increment mg ha‒1 yr‒1
Species diversity Shannon diversity Shannon’s index
NH+
4
Ammonia ( ) termed as nitrogen Nmg kg‒1
Phosphorus P mg kg‒1
Potassium K mg kg‒1
Nutrients Nutrients Total nutrients=N+P+K
PAR leaf area index LAI (m2 m‒2)
4 Mangroves buffers against salinity
tree height in meters, and the quadratic mean diameter at
breast height (DBH), i.e., 130 cm. We employed Shannon’s
index to evaluate species diversity (our survey covered 13
species). We calculated the coefficient of variation of H and
DBH to analyze structural diversity, specifically the distribu-
tion of tree sizes. We also determined the Leaf Area Index
(LAI) to check the impacts of functional traits on tree growth.
We collected five LAI readings from each plot to determine
potential tree variability. Lastly, we estimated tree above-
ground biomass non-destructively using allometric equa-
tions, which had been developed locally to estimate dry
biomass for all tree species (Rahman et al., 2021b) (Table
S1). Additionally, to assess the influence of nutrient avail-
ability, we examined the presence of nitrogen, phosphorus,
and potassium within the top 50 cm of soil depth. The above
methodology employed is based on the approach outlined in
Ahmed et al. (2022).
2.3 Statistical analyses
In our analyses, we started by checking the normality of our
dataset using a Shapiro‒Wilk normality test. To address any
deviations from the distribution of variables, we made a
logarithmic transformation to align them with the assumptions
of normality and homogeneity, if necessary. Moving on to
exploring the first research question (QI), we initially looked
at how forest growth, salinity, density, structural diversity,
and species richness correlate through linear regression
using the “lm” function in R.
Furthermore, to elucidate the relative impacts of species
diversity and structural diversity (direct vs. indirect) on miti-
gating salinity effects on AGB growth across varying nutrient
levels, we employed a structural equation model (SEM)
utilizing the “lavaan” package (Rosseel, 2012) and plotted
the model by “tidySEM” package Van Lissa (2020). To
determine the most suitable model, we employed various fit
indices, including the chi-square (χ2) test (with the criteria of
0≤χ2/df≤2), the comparative fit index (CFI) (requiring
CFI>0.95), the standardized root mean square residual
(SRMR<0.05), and the significance of paths (p>0.05), as
recommended by Schermelleh-Engel et al. (2003). We
calculated the indirect and total association of sediment
salinity and Shannon diversity with aboveground biomass
growth by following Rahman et al. (2021a).
As we delved deeper into our analysis to test HII, we
performed several multiple linear models (MLM) using the
“lm” function (global model) by choosing variables from the
SEM in a progressive way. We employed an MLM with stan-
dardized predictor variables. Standardization was performed
using the formula f(x) = (xi‒xmin)/(xmax‒xmin), where xi repre-
sented the variables with their respective maximum (xmax)
and minimum (xmin) values. This approach facilitated a direct
comparison of effect sizes, thereby enhancing the inter-
pretability of the model (Schielzeth, 2010). Finally, we opti-
mized all models to maximize the growth by reducing salinity
impacts. We used “optim” functions with advanced optimiza-
tion methods “L-BFGS-B” in base R. Similar to model fitting,
we first tried to optimize the model without considering nutri-
ents and leaf area index , as forest structure and species
composition management are easily attainable through
management (Pretzsch, 2009). We expected that the impact
of nutrients might manifest in structure and diversity. In the
second and third optimization steps, we included nutrients
and leaf area index in the model. We compared the modelʼs
performance (i.e., with vs. without nutrients and leaf area
index based on AIC values by using the “performance” func-
tions from the “performance” package (Lüdecke et al.,
2021). Besides, all the visualizations were done by using the
“ggplot2” package (Wickham, 2011).
All statistical analyses and visualizations were performed
in R (version 4.3.1) (R Core Team, 2023).
3Results
3.1 Patterns and distribution of species and structural diver-
sity across salinity, nutrient availability, and density gradients
Stand structure, species diversity, and growth varied across
salinity zones and levels as well as stand density (Fig. 2).
Most structural diversity, such as H, DBH, cv of H, and DBH,
were higher, and distributions were skewed towards the
oligohaline and mesohaline zones. In contrast, species
diversity tends to be higher in the polyhaline salinity zones
(Fig. 2A‒2E). Stand structural properties and forest growth
(above-ground biomass changes over time) varied across
salinity and density gradients (Fig. 2). Forest growth was
reduced with salinity. In contrast, growth increased with
nutrient availability and density (see the trend in Fig. 2F‒
2H).
3.2 Role of structural and species diversity in modulating
salinity impacts on growth via nutrient availability and
functional traits (Q1)
Species diversity enhanced growth across salinity levels but
declined under varying nutrient regimes and density gradients
(Fig. 3, right column). Structural diversity (measured as the
coefficient of variation of DBH) positively influenced growth
across nutrient regimes and density gradients but decreased
with increasing salinity levels (compare the trends, Fig. 3,
middle column). Additionally, AGB growth increased with
AGB stocks (Fig. S2). Growth responses to structure and
species density across salinity zones are presented in
Fig. S3.
Figure 4 shows that growth response varies by species
Shamim Ahmed et al. 5
and is influenced by functional traits. Growth was positively
correlated with mean tree height (H), diameter at breast
height (DBH), and LAI, with species-specific variations
observed across nutrient availability and salinity levels.
Our Structural Equation Model (SEM) demonstrated a
good fit (χ²=12.889, p=0.301), providing insights into how
species diversity and structural diversity influence the rela-
tionship between salinity and growth via nutrient availability
and leaf area index (see Fig. 5). The SEM accounted for
84% of the variation in Aboveground Biomass (AGB)
growth. Specifically, species diversity had a significant direct
effect on growth (β=0.16, p<0.01) and an indirect impact by
improving leaf trait levels (β=0.10, p>0.05). In contrast,
structural diversity showed a positive and statistically signifi-
cant direct effect on growth (β=0.23, p<0.01).
As expected, salinity was found to be negatively correlated
with nutrient availability (β=−0.34, p<0.001) and showed a
weak, non-significant direct effect on AGB growth (β=−0.02,
ns). This suggests that salinity indirectly affects AGB growth
by reducing nutrient availability and LAI rather than having a
pronounced direct impact. The LAI had a strong, positive,
and statistically significant direct effect on AGB growth
(β=0.71, p<0.001), while LAI elevated with nutrients (β=0.40,
p<0.001).
The total effect of sediment salinity was negative
(β=−0.309, p<0.01), while species diversity (β=0.07, p>0.05)
and nutrients (β=0.156, p>0.05) had positive but not signifi-
cant effects (see Table 2).
3.3 Role of structural heterogeneity and species diversity to
create optimal growth conditions (Q2)
The optimized modelʼs results, as shown in Fig. 6, highlight
how—species diversity, structural diversity, nutrients, and
LAI—influence forest growth in the presence of salinity
stress. Fig. 6A shows that predictions fall below zero when
only species diversity and cv of DBH are included in the
model. Fig. 6B indicates a reduction in the negative trend
when nutrients are added, with predictions hovering near the
zero line. Figure 6C demonstrates that including LAI results
in most predictions exceeding zero.
4Discussion
Our results indicate that structural and species diversity
reduces salinity impacts by associating with nutrient
availability and leaf area index. More precisely, structural
diversity is more effective in low-salinity areas where
competition for light is intense. Species diversity matters
more in high-salinity areas where usable water for trees is
scarce. Besides, nutrient availability improves forest condi-
Fig.2 The descriptive representations of patterns of structure and AGB growth. Top row: (A‒E) shows cumulative density
distributions of structural properties (A) mean DBH, (B) mean height, (C) horizontal diversity or coefficient of DBH, (D) mean
coefficient of height, (E) Shannon species diversity. In the Bottom row, figures (F‒H) show growth response. (F) growth
response to different salinity levels, (G) growth response to nutrient availability, and (H) growth variability with different stand
density levels. In Fig. F‒H, vertical solid lines indicate a 95% confidence interval and black circles indicate median values.
6 Mangroves buffers against salinity
tions by helping leaf function traits, further helps the forest
reduce salinity stress and competition for light by modifying
structure and species.
4.1 Role of structural and species diversity in modulating
salinity impacts on growth via nutrients, functional traits, and
stand density (Q1)
Our results indicate that forest growth predominantly
occurred in lower salinity areas (Fig. 2), driven by salinityʼs
impact on stand structural diversification. High salinity areas
showed lower overall growth, likely due to reduced hydraulic
conductivity and water availability (Lovelock et al., 2006).
Besides, it could also be possibly due to low stand dominant
height and stocking degrees (Condés et al., 2013) in high
salinity areas.
Mangrove growth’s multidimensional response to salinity
was observed through tree structure (DBH, cv of DBH),
species diversity, nutrient availability, and stand density
(Figs. 3−4). Bivariate relationships showed species diversity
had a negative and insignificant impact across salinity gradi-
ents, but SEM indicated a positive impact on AGB growth
when interacting with nutrients and leaf area index. High
salinity may increase mortality and create gaps, potentially
filled by saline-tolerant species like C. decandra (Ahmed
et al., 2023), increasing species diversity without enhancing
growth. Nutrient levels did not increase with species diversity
as expected, possibly due to flash flooding or distinct pheno-
Fig.3 Growth response to stand structure and species diversity across different salinity, nutrients, and stand density levels.
SD indicates stand density (stems ha‒1). The Grey dashed line indicates the mean trend line.
Shamim Ahmed et al. 7
logical patterns or terrestrial inputs. We expected continuous
litterfall from various species most likely enriched the soil
with nutrients (Ahmed and Kamruzzaman, 2021), and
diverse root depths allowed better nutrient utilization
(Soethe et al., 2006). This variation could be attributed to
differences in nutrient availability across salinity gradients. A
previous study highlighted that highly saline, less-diverse
Sundarbans mangroves are characterized by elevated phos-
phorus (P) and potassium (K) levels, whereas less saline,
more diverse mangrove communities exhibit higher nitrogen
(N) levels. Notably, nitrogen appears to have minimal influ-
ence on community diversity (Sarker, 2017). These patterns
are likely associated with the location of these areas—lower
salinity zones are typically situated near estuaries that
receive terrestrial nutrient inputs, while higher salinity zones
are found along open coasts with limited or no terrestrial
nutrient inputs. Additionally, litterfall decomposition rates
and microbial activity tend to be higher in lower salinity
areas (Khan et al., 2009; Alongi, 2011), contributing to
enhanced nutrient availability.
Besides, these contrasting results indicate that forest func-
tions might species specific and their response to with
disturbance or stress type (i.e., competition for nutrients,
light or salinity) (see Figs. 3, 4 and Sarker et al. (2021)).
Structural diversity positively correlated with AGB growth
but negatively with nutrient levels, suggesting smaller trees
benefit more from nutrients. Structural diversity and functional
traits had a more substantial influence on biomass growth
than species diversity (Fig. 5), possibly due to synchrony or
disturbances like cyclones. Species diversity’s weak correla-
tion with biomass growth may explain species loss with
stand maturity in high saline areas (Schall et al., 2018;
Sarker et al., 2019a; Ahmed et al., 2022). LAI strongly influ-
enced AGB growth, enhanced by nutrient availability (Pérez-
Ramos et al., 2019; Yaseen et al., 2023), which is consistent
with our results.
Fig.4 The changes in AGB growth concerning tree and species level structure (H, DBH), leaf area index (LAI), and varying
levels of salinity and nutrient availability. The pink color indicates the mean trend line. Legends are applicable for all sub-figures.
Fig.5 The Structural Equation Model (SEM) illustrating the
nuanced contributions, both direct and indirect, of species diversity
and structural diversity in mitigating the impact of salinity on growth
along varying nutrient gradients. The goodness-of-fit tests for the
model produced the following outcomes: χ2=12.889, p=0.301,
indicative of a commendable fit with the data. Moreover, the
Comparative Fit Index (CFI) registered at CFIc 0.988, while the
Root Mean Square Error of Approximation (RMSEA) and Standard-
ized Root Mean Square Residual (SRMR) were 0.053 and 0.066,
respectively, signifying no significant deviations from the model’s
underlying dataset. These assessments were conducted with a
degree of freedom of two. The numerical values on the arrows
denote the standardized strength of the relationships between the
predictors and the dependent variables. The figures adjacent to the
variables signify the proportion of variance accounted for, quantified
as the coefficient of determination (R2) is denoted by %, by all the
predictors. The thickness of the lines signifies the magnitude or
robustness of the relationships. Moreover, the proximate path
values indicate standardized path coefficients and incorporate
significance levels denoted by asterisks (ns>0.05; *p<0.05;
**p<0.01; and ***p<0.001). ns, not significant.
8 Mangroves buffers against salinity
Table2 Indirect and total standardized association of sediment salinity, nutrients, functional traits, species, and structural diversity on forest
increment.
Indirect and total association pathways Increment
std. as p-value
Indirect association of sediment salinity through Shannon diversity 0.052 0.105
Indirect association of sediment salinity through LAI −0.232 0.016
Indirect association of sediment salinity through nutrients −0.153 0.062
Indirect association of sediment salinity through cv of DBH −0.015 0.623
Indirect association of sediment salinity through Shannon diversity and LAI 0.022 0.432
Indirect association of sediment salinity through Shannon diversity and cv of DBH 0.007 0.454
Indirect association of sediment salinity through Shannon diversity and nutrient −0.01 0.646
Indirect association of sediment salinity through Shannon diversity, nutrient, and LAI −0.007 0.647
Indirect association of sediment salinity through Shannon diversity, nutrient, and cv of DBH 0.003 0.654
Indirect association of Shannon diversity through cv of DBH −0.095 0.045
Indirect association of Shannon diversity through LAI 0.068 0.411
Indirect association of Shannon diversity through nutrients −0.05 0.639
Indirect association of Shannon diversity through nutrients and LAI −0.02 0.642
Indirect association of Shannon diversity through nutrients and cv of DBH 0.009 0.65
Indirect association of nutrients through cv of DBH −0.127 0.084
Indirect association of nutrients through LAI 0.283 0.004
Total association of sediment salinity −0.309 0.008
Total association of Shannonʼs diversity 0.071 0.552
Total association of nutrients 0.156 0.17
The indirect and total associations of sediment salinity, nutrients, and Shannon diversity were based on structural equation models. The
significant standardized associations (std. as) have a p-value<0.05.
Fig.6 Model optimization to maximize forest increment or AGB growth. In A‒C, the x-axis represents the salinity values, and
the y-axis represents the difference in increment values (the predicted growth values obtained from the optimized settings for a
specific set of predictor variables−the actual AGB growth values or the original data). The blue line shows how the contrast
changes as salinity changes. The magenta dashed line at y=0 indicates where the optimized and actual increment values are
equal. If the blue line exceeds the red dashed line, optimized settings reduce salinity’s impact on AGB growth; minimal difference
suggests predictor variables have limited influence on the outcome. In the subset, respective models optimize the settings, and
predictions are presented.
Shamim Ahmed et al. 9
Growth increased with forest stand density, despite SEM
not including density in the final model. Higher density
fostered greater height growth in young trees, especially in
high salinity areas (Fig. 2). Heightened competition for light
among densely packed trees led to increased aboveground
biomass (López-Hoffman et al., 2006; Peng et al., 2022).
Biodiversity and nutrient richness in densely populated
stands also contributed to growth (Vanelslander et al., 2009;
Cleland, 2011).
However, structural diversity, nutrients and leaf trait posi-
tively buffered salinity impacts, while they are interacting
together.
4.2 Role of structural heterogeneity and species diversity to
create optimal growth conditions (Q2)
Our model optimization settings explain that combined struc-
tural diversity and species proportions may not maximize
forest growth. However, adding nutrients to optimization
settings enabled the forest to buffer a bit (until a certain
salinity level was reached). Still, when added functional trait
optimization showed the forest to sustain its growth against
salinity (see Fig. 6). These results indicate that a nutrient-
free setting may not mitigate salinity impacts and nutrient
availability most likely trees to capture light and accumulate
biomass efficiently through functional traits. However,
continuously increasing salinity led to a reduction in growth,
eventually reaching the zero line. This supports our second
hypothesis (HII). These results additionally indicate that
species diversity and structural diversity, coupled with
canopy complementarity, assist trees in effectively utilizing
the available nutrients and mitigating the impacts of salinity.
This could be related to more plastic and extensive crown
space occupation by species-diverse stands compared to
less diverse stands, which could increase aboveground
biomass in higher salinity stands. It could be attributed to
species diversity and density, which increased canopy pack-
ing, reduced crown shyness, and enhanced light use effi-
ciency, leading to increased growth (Pretzsch, 2014; Jucker
et al., 2015). However, the mean line suggests that better
outcomes (growth) may be attainable by adjusting predictor
variables, i.e., increasing canopy packing through mixing.
Several studies have demonstrated that tree density
management and thinning can increase forest growth by
lowering competition for resources like light, water, and
nutrients (Miller, 1997; Güney et al., 2022; Qu et al., 2022).
Nevertheless, sustainable forest growth needs modified
management strategies in response to the effects of climate
change (Millar et al., 2007; Lindenmayer et al., 2011). Our
model optimizations also indicate that structure and the
growth of stands can be enhanced under stress by applying
suitable silvicultural strategies. By integrating these principles
and practices into forest management, forest ecosystemsʼ
ecological health and resilience can be preserved while
maximizing growth. However, large-scale datasets or more
studies are required to conclude.
5Researchimplicationsandfuturedirections
The findings of our study shed light on the crucial role of
species and structural diversity in mitigating the impact of
salinity, particularly in regions with higher salinity levels. We
observe a buffering effect in the areas where species diversity
is naturally more abundant. This suggests that the functional
differences between species, specifically their varying levels
of salt tolerance, work synergistically to support continued
forest growth.
Conversely, in regions with lower salinity levels but higher
structural diversity, we see a different dynamic at play. Here,
the growth persists, indicating that species diversity might
not be the primary driver. Instead, less salty areas and salt-
tolerant species, while maintaining structural diversity, tend
to utilize crown space more extensively through denser
canopy packing. This might lead to an increase in above-
ground biomass growth.
Besides, in the case of the natural mangrove forest,
managing both the structure and species composition
becomes critical due to its vast expanse. However, this
insight is particularly relevant in specific natural forest parts
where natural mortality rates tend to be higher (could be due
to salinity, biological age, or natural calamities). More
precisely, stands-dominated pioneer species like Sonneratia
apetala and Avicennia officinalis often experience frequent
mortality due to their biological age, or diseases like dying
back increase mortality (Giri et al., 2007; Rahman et al.,
2010) and form significant gaps. Furthermore, the Sundar-
bans region is prone to tropical cyclones, which lead to tree
mortality by causing severe damage, further creating gaps,
especially in areas with higher salinity levels, which are
more exposed to the coasts with little or no terrestrial nutrient
inputs. Introducing different species in these gaps could be
beneficial in expediting forest recovery and enhancing
resilience against future climatic events (natural calamities
and salinity).
This research holds strong applicability in coastal single
species-based plantation efforts to mixing plantations, espe-
cially when the objective is to safeguard coastal areas while
simultaneously harnessing the co-benefits of carbon
sequestration (growth under salinity stress) in the face of
cyclones. It underscores the potential of nature-based solu-
tions in fortifying our coastal belts against environmental
challenges.
However, our study was conducted in a single mangrove
forest (Sundarbans), which may limit generalizability to other
10 Mangroves buffers against salinity
regions. Examining multiple sites across varying salinity
gradients could strengthen conclusions. Besides, we expect
some interplay between water depth and seasonal rainfall
patterns due to storms effect on salinity level and impacts,
which could be interesting to explore. However, our dataset
could not capture any disturbance (e.g., cyclones) effect on
growth across species within study periods (~2.5 years).
Overall, the study takes an essential first step in highlighting
biodiversity’s role in increasing mangrove resilience to salinity
stress. However, ongoing research across broader spatial
scales and including additional functional measures is
needed. For example, the mechanisms underlying diversity-
productivity relationships still need to be fully elucidated.
Further research into functional traits and niche differentia-
tion could provide more mechanistic explanations. Transla-
tion to the application requires examining feasibility
constraints. A systems perspective incorporating socio-
ecological factors could maximize conservation impact.
While promising, the findings represent an early stage in
fully elucidating diversity’s multifaceted influence.
6Conclusion
This study evaluated the comparative role of structural and
species diversity in modulating salinity impacts on growth.
We also assessed how some growth-modifying factors, for
example, nutrient availability, and leaf functional traits,
modulate the salinity-growth relationships. Our analyses
revealed that the growth of mangrove forests is strongly
controlled by salinity, with its effects mediated by structural
heterogeneity, nutrient dynamics, and leaf functional traits.
Conversely, the leaf area index emerged as the primary
facilitator of forest growth in areas marked by elevated salinity
and limited water access. While species diversity may influ-
ence growth indirectly through its interaction with forest
structure, structural diversity, including variations in stand
size classes and horizontal heterogeneity. These insights
are vital for improving mangrove growth modelling, enhancing
ecological understanding, and informing coastal forest
conservation and management strategies.
Acknowledgements
We are grateful to the Bangladesh Forest Department for allow-
ing us to establish permanent sample plots. Our sincere gratitude
goes out to our study assistants, who helped us with data
collection. We also used BioRender (TUM licensed version) to
create Figure 1 and Figure 5 (BioRender.com). The work was
funded by BANBEIS, Ministry of Education, Government of the
People’s Republic of Bangladesh (Reference: LS2023-2382),
the Grant-in-Aid for Scientific Research from Research and
Innovation Centre, Khulna University, Khulna-9208, Bangladesh
and SUST Research Centre (Project Id: FES/2021/1/02). Open
Access funding enabled and organized by Projekt DEAL.
Authorscontributions
Conceptualization: SA; Data collection: SA, SKS (lead) and MK;
Data analysis: SA (lead), MMR; Manuscript writing: SA (lead),
RH, NT, SP; Manuscript revision: MMR, HP, SKS, SP, TH, TTA,
EU, CSSN; Supervision: HP (lead), SKS
Competinginterest
The authors declare no competing interest.
Dataandcodeavailability
Data is available in Ahmed et al. (2022). All R code is available
from the corresponding author upon request.
Electronicsupplementarymaterial
Supplementary material is available in the online version of this
article at https://doi.org/10.1007/s42832-025-0299-x and is
accessible for authorized users.
OpenAccess
This article is licensed under a Creative Commons Attribution
4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and
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a copy of this licence, visit http://creativecommons.org/licenses/
by/ 4.0/.
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14 Mangroves buffers against salinity
