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Open Access https://doi.org/10.48130/seedbio-0024-0020
Seed Biology 2024, in press
Climate, soil, and stand factors collectively shape the macroscopic
differences in soil seed bank density between planted and natural
forests
Jiangfeng Wang1#, Ru Wang1#, Xing Zhang1#, Jiali Xu1, Xueting Zhang1, Xiali Guo2* and Jie Gao1,3*
1Key Laboratory for the Conservation and Regulation Biology of Species in Special Environments, College of life science, Xinjiang Normal University, Urumqi,
830054, China
2Guangxi Key Laboratory of Forest Ecology and Conservation, State Key Laboratory for Conservation and Utilization of Agro-bioresources, College of Forestry,
Guangxi University, 530004, Nanning, China
3Key Laboratory of Earth Surface Processes of Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100863, China
# Authors contributed equally: JiangfengWang, RuWang, XingZhang
* Corresponding authors, E-mail: guoxl6666@hotmail.com; jiegao72@gmail.com
Abstract
Global climate change is intensifying forest degradation, making the soil seed bank density (SSBD) in planted and natural forests a crucial
resource for ecosystem restoration. Focusing on soil seed bank density can help us assess the potential of vegetation regeneration and maintain
ecosystem stability and function. However, the macro-scale distribution differences and controlling mechanisms of SSBD in these forests remain
elusive. This study focuses on the SSBD in 537 natural and 383 planted forest sites across China, examining the specific impacts of climatic, soil,
and forest stand factors. This study also predicts the pathways through which these factors modulate SSBD variations in both forest types. Our
findings reveal that SSBD is significantly higher in planted forests compared to natural ones (P < 0.001). SSBD shows a marked declining trend
with increasing temperature and precipitation (P < 0.001). In contrast, increases in sunlight duration and evapotranspiration positively correlate
with SSBD in both forest types. Natural forests exhibit higher sensitivity to soil nutrient changes than planted forests. Both forest types show
similar SSBD trends with changes in forest stand factors. Soil pH independently contributes the most to the spatial variation of SSBD in natural
forests, while soil nitrogen content is the most significant contributor for planted forests. Mean Annual Temperature (MAT) and Mean Annual
Precipitation (MAP) not only directly affect SSBD in natural forests but also indirectly through soil pH, forest stand density, and forest net primary
productivity, with direct impacts outweighing the indirect. In planted forests, Mean Annual Evapotranspiration (MAE), Mean Annual Precipitation
(MAP), soil nitrogen content, and stand density have a direct and significant impact on SSBD. Additionally, MAE and soil nitrogen content
indirectly affect SSBD through forest stand density. Our results reveal that in forest management and administration, attention should not only be
given to changes in climatic factors but also to soil nutrient loss.
Citation: Wang J, Wang R, Zhang X, Xu J, Zhang X, et al. 2024. Climate, soil, and stand factors collectively shape the macroscopic differences in soil
seed bank density between planted and natural forests. Seed Biology https://doi.org/10.48130/seedbio-0024-0020
Introduction
Soil seed banks are a crucial component of forest ecosys-
tems, directly influencing ecosystem structure and function, as
well as the assembly and succession of forest communities[1]. It
remains unclear whether there are significant linear differences
in forest soil seed bank abundance along geographical scales[2].
Additionally, forest community assembly patterns differ
between different forest origins (planted forests vs. natural
forests), and it is uncertain whether these origin differences
affect soil seed bank density (SSBD)[3]. Therefore, investigating
the distribution patterns and key factors influencing soil seed
density between planted and natural forests ecosystems at a
macro scale is of great significance for sustainable forest
management.
Natural forests are characterized by a series of successional
stages of plant communities that develop on primary or
secondary bare land[4]. Dominated by native species, these
forests have the ability to regenerate naturally, boasting
complex ecosystems and high biodiversity[5]. In contrast,
planted forests are predominantly created through artificial
sowing, cultivation, and management, exhibiting uniform age
and simplified structure due to human intervention[6].
Compared to natural forests, planted forests generally exhibit
lower biodiversity and diminished ecosystem functions[5].
Within both natural and planted forest ecosystems, soil seed
banks play a crucial role in maintaining population size and
diversity through temporal storage effects[7]. Soil seed banks
have the ability to restore degraded ecosystems and accelerate
forest succession, so the renewal of natural and planted forests
is largely dependent on soil seed banks[8]. Consequently,
understanding the dynamics of soil seed banks is of paramount
importance in forestry, as it provides valuable insights into the
natural regeneration of forests, guiding future forest manage-
ment practices.
Numerous studies have highlighted the pivotal role of
climatic factors, notably temperature and precipitation, in regu-
lating the growth of both planted and natural forests, as well as
in resource allocation[3,9]. Likewise, a substantial body of
ARTICLE
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research indicates that temperature and precipitation primarily
drive the variances in soil seed banks at the macro scale[10−12].
Hence, climatic factors may exert significant impacts on the soil
seed bank of both planted and natural forests. As temperatures
rise and rainfall increases, soil seed density significantly
decreases[13]. This is mainly because, with higher temperatures
and increased precipitation, trees adopt a strategy of rapid
investment-reward in resource utilization, leading to faster
growth, enhanced inter/intra-species resource competition,
increased investment in resource competition, and reduced
investment in reproduction. This ultimately results in lower
levels of soil seed density[14]. Daylight duration also significantly
affects soil seed density, as different daylight durations can
alter plant growth cycles, influencing flowering and fruiting
times, thus impacting seed production and density[15]. Further-
more, changes in light conditions can affect competitive rela-
tionships among plant species, with some plants being more
adapted to longer periods of sunlight, while others may have a
competitive advantage under shorter light conditions[16]. Such
differences can influence species survival and reproduction,
subsequently affecting the density of seeds in the soil.
Soil, as the direct living environment for trees, plays a crucial
role in their growth, development, and reproduction[17,18].
Research has also found that the resource allocation strategies
of both planted and natural forests are significantly constrained
by soil nutrients[3]. Therefore, soil nutrients may represent
another type of abiotic factor that limits the soil seed bank of
forests. Under conditions of ample nutrients, plants may
produce more seeds, increasing seed density[19]. Additionally,
soil nutrients influence seed viability and germination
capacity[20]. The nutritional status of the soil can also affect soil
seed density by influencing competitive relationships among
plant species[21]. In nutrient-rich environment, competitively
dominant species may prevail, whereas in nutrient-poor envi-
ronment, species with strong adaptability may have a better
chance of survival. These differing competitive pressures can
impact the density and abundance of species in the seed
bank[22]. Soil nutrients also influence the activity of soil microor-
ganisms, which, in turn, affect the physical and chemical prop-
erties of the soil, subsequently impacting seed survival and
germination[23].
In different developmental stages of forests, trees exhibit
various reproductive strategies, which may consequently have
an impact on the soil seed bank. Many studies found that forest
stand characteristics, such as stand age, mean diameter at
breast height, key leaf traits, and forest productivity, can influ-
ence SSBD[24−26]. With increasing stand age, the intensity of
interspecific competition can change, and the microenviron-
ment within the forest, including factors like light, humidity,
and soil structure, can undergo alterations, subsequently affect-
ing the soil seed bank[27]. In recent years, numerous studies
have highlighted the critical role of key leaf traits in explaining
various ecological phenomena. Species with higher specific leaf
area (SLA) and lower leaf dry matter content (LDMC) tend to
adopt a fast investment-reward resource utilization strategy,
allocating more resources to interspecific competition and
reducing investment in reproduction[18]. This leads to lower
levels of soil seed bank density. Similarly, some research has
found that forests with higher productivity typically have
greater biomass, resulting in more seed production and
increased seed bank density[28]. However, it should be noted
that forests with higher productivity may also experience more
intense interspecific competition, which can lead to lower soil
seed density[27].
In this study, based on SSBD data collected from 537 natural
forests and 383 planted forests within China through field
surveys and literature sources, we aim to investigate the differ-
ences in SSBD between plantation and natural forests at the
macro scale and the key factors driving these differences. To
address these questions, we make the following hypotheses: (1)
SSBD in planted forests will significantly exceed that in natural
forests; (2) Climatic factors will be the primary drivers of the
macro-scale differences in SSBD between planted and natural
forests; (3) Climatic factors will influence SSBD in planted and
natural forests by adjusting soil nutrients and stand characteris-
tics.
Materials and methods
Soil seed bank density data
The density data of the soil seed bank were collected partly
from literature searches and partly from field measurements.
The specific data are listed in Table S1. We searched for relevant
peer-reviewed journal articles published between 2005 and
2020 in Web of Science, Google Scholar, and CNKI. The keyword
combinations used in the search were "forest" and "soil seed
bank." A total of 108 relevant papers containing 623 data points
were retrieved. We then screened the data in the literature
using the following criteria: (1) the latitude and longitude of the
plots should be provided by the study, and the plots should be
categorized as either natural or planted forests; (2) the study
should provide or allow the calculation of the mean, standard
deviation, or standard error of soil seed bank density data in the
sample plot; (3) the study should present the results of field
studies rather than retrospective or simulation studies. (4) the
sampling period should be outside of peak germination
seasons to minimize seasonal effects on soil seed bank density
estimates. For the articles meeting our criteria, the index of soil
seed density in the 0-10 cm soil surface layer was extracted. If a
study has multiple sampling depths from 0 to 10 cm at the
same site, we treated these observations as independent
samples. In these articles, we collected as much information as
possible on tree species, stand age, stand density, tree DBH,
and other stand characteristics of each sample plot.
We selected 27 sites in the field and measured data from 297
forest plots. We recorded the latitude, longitude, elevation, and
slope of each site for comprehensive analysis, and documented
the site location, tree species, stand age, tree DBH, and stand
density in real-time. At each site, at least four 20 × 20 m forest
plots with typical zonal vegetation were selected. For sampling,
we used the same method as described in the literature to
measure soil seed bank density: after removing litter from the
surface of each sample plot, five soil samples, each with dimen-
sions of 10 cm × 10 cm × 10 cm, were randomly collected. The
litter layer was removed to focus on the persistent soil seed
bank in the 0-10 cm soil layer, minimizing the effects of short-
term seed input and ensuring consistency across sites. The
samples were thoroughly mixed and then placed in soil bags,
which were sieved to remove debris upon return to the labora-
tory. The samples were stored in a dry, dark environment until
germination experiments began in May of the following year. In
May, the labeled soil was evenly spread in germination trays to
soil seed bank density
Page 2 of 11 Wang et al. Seed Biology 2024, in press
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a depth of about 5 cm. Iron arches were set up over the trays
and covered with film to prevent external seeds from entering.
The temperature inside the enclosure was maintained at 25-
30°C, with natural light and a humidity level of around 70%.
Water was applied every 3-5 days to keep the soil moist. The
germination and growth of the seeds were observed and
recorded. From the onset of sprouting, daily records were kept
of the number of seedlings and their morphological character-
istics. Finally, the remaining seeds in the germination trays
were checked for germination. The remaining ungerminated
seeds were tested for viability using the tetrazolium chloride
(TTC) staining method, with seeds soaked in a 1% TTC solution
at 30 °C for 24 hours. Seeds that displayed a reddish color in
their embryos were considered viable[29]. For seeds that did not
show a clear TTC staining result, manual examination was
performed by cutting to check for intact embryos. The seed
bank germination experiment lasted from May to November of
the following year. The number of seedlings for all species
recorded during the experiment was used to calculate the seed
bank density, expressed as the number of seedlings per unit
area, for further analysis.
Climate data
Climate data, including Mean Annual Temperature (MAT),
Mean Annual Precipitation (MAP), Annual Sunlight Duration
(ASD), and Mean Annual Evaporation (MAE), were obtained
from WorldClim (https://worldclim.org/) at a 1 km spatial reso-
lution.
Soil nutrient data
We extracted data for total nitrogen content and total phos-
phorus content of 0-20 cm soil at a 1 km resolution from the
Harmonized World Soils Database version 2.0
(https://gaez.fao.org/pages/hwsd).
Forest stand factors
Forest stand factors include forest age, forest density, and
forest mean diameter at breast height (DBH). Forest age is
mainly obtained from the literature reviewed. For literature
without forest age information, we referred to local forestry
bureau and ecological station websites, as well as consulted
with specific personnel in charge. The forest DBH represents
the average DBH of all trees (DBH > 5cm) within the plot. For
species identification, we relied on local flora references, and
for species that were difficult to classify, we referred to the WFO
Plant List (https://wfoplantlist.org/) to confirm taxonomic
status. For each sample plot, we randomly selected five domi-
nant trees based on their relative dominance (e.g., height and
canopy spread) to represent the primary structural characteris-
tics of the stand. The selected trees could either be from the
same species or different species, depending on the composi-
tion of the plot. To minimize sampling bias, we excluded trees
with abnormal growth patterns. Stand-level measurements,
such as forest density, were calculated as the number of indi-
vidual trees per unit area, and species diversity was determined
based on the identified species in each plot. Altitude, slope,
aspect, and other stand factors of our actual survey plots were
measured using handheld GPS devices. Due to the limited
number of actual field survey plots, stand factors such as altitude
were not considered in the subsequent calculations.
Plant functional traits
In this study, five plant functional traits were selected to
represent diverse strategies of plant resource utilization: leaf
area (LA), specific leaf area (SLA), leaf dry matter content
(LDMC), leaf nitrogen content (LN), and leaf phosphorus
content (LP). The data on plant functional traits of regional tree
species collected in the literature were obtained from the TRY
database[30]. During field measurements, five dominant trees
were randomly selected from each sample plot, ensuring they
were situated away from plot edges. Leaves were collected
from various directions at the same height in the middle of the
canopy of each selected tree. Twenty leaves of similar maturity,
free from diseases and pests, were gathered and stored in
ziplock bags for transport to the laboratory. Upon arrival at the
laboratory, leaf area (LA) was measured using a portable laser
planimeter (CI-202, Walz, Camma, USA)[31]. Subsequently, the
leaves were submerged in water and placed in a dark environ-
ment at a constant temperature of 4 °C for 12 hours. Once the
surface water was absorbed, the saturated fresh weight of the
leaves was measured using an electronic balance. The leaves
were then placed in an oven at 120 °C for 30 minutes, followed
by drying at 80 °C for 24 hours, and the dry weight of the leaves
was recorded. Leaf nitrogen content was determined using the
Kjeldahl method, while leaf phosphorus content was measured
using the Mo-Sb colorimetry method[32]. Specific leaf area (SLA)
and leaf dry matter content (LDMC) were calculated using the
following formulas: Specific leaf area (SLA) = leaf area / leaf dry
weight; leaf dry matter content (LDMC) = leaf dry weight / leaf
saturated fresh weight. While acknowledging potential differ-
ences in plant functional traits among species, this study
focused on exploring these traits at the community scale.
Therefore, the community weighted mean value (CWM) was
utilized to represent the average trait value of each plot.
CWM =
S
∑
i=1
Di×Traiti(1)
where, CWM denotes community weighted functional trait
values, Di is the abundance of dominant species, and Traiti is the
specific functional trait[33].
Forest net primary productivity data (NPP)
We obtained China's MOD17A3H vegetation net primary
productivity (NPP) data from the NASA website
(https://search.earthdata.nasa.gov/search), with a spatial scale
of 500m and a time scale of years. The NPP estimates were
generated using the Carnegie-Ames-Stanford Approach (CASA)
model, employing the following calculation method:
NPP(x,t) =APAR(x,t) ×ε(x,t) (2)
where APAR(x,t) represents the photosynthetically active
radiation (PAR) absorbed at the x pixel in the t-th month, with
units in MJ/m². ε(x, t) represents the actual light use efficiency at
the x pixel in the t-th month, measured in g·C/MJ[34].
Analysis
Initially, we transformed the soil seed bank density data loga-
rithmically to normalize it, and all subsequent analyses were
performed using these log-transformed data. Prior to analysis,
all variables were standardized for a comparable scale in inter-
preting parameter estimates.
All data analyses were conducted using R (version 4.2.2,
www.R-project.org). We used the 'ggsignif' package to test the
difference in soil seed bank density between natural forests and
planted forests at the 0.05 significance level. To reduce
soil seed bank density
Wang et al. Seed Biology 2024, in press Page 3 of 11
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collinearity among multiple plant functional traits, we
employed the 'pcaMethods' package for PCA analysis of plant
functional traits and extracted the first two principal compo-
nents, PC1 and PC2[35]. The general linear regression model in
the 'lme4' package was utilized to analyze the effects of climate,
soil, and plant factors on soil seed bank density in plantations
and natural forests, with R² used to evaluate model fitting[36]. To
visualize the relationship between various factors and soil seed
bank density, we created a correlation heat map using the
'linkET' package[37].
We construct a multiple linear regression model, based on
modified Akaike information criteria (AICc; ΔAICc < 2) selection
procedure to select the best predictors of soil seed bank
density. We used the 'dredge' function in the MuMIn package
to create all possible subset models, ranking them based on
their AICc values (AIC value corrected for sample size), and
selecting the model with the lowest AIC value as the optimal
model[38]. The contributions of various factors in the optimal
model to SSBD were recorded. Variance decomposition was
then performed using the rdacca.hp function, assessing the
variance contributions of climatic, soil, and plant factors to the
optimal model, expressed as percentages[39].
Structural equation models (SEM) can be used to evaluate
complex causality between variables by translating hypothetical
causality into the expected statistical relationship pattern in the
data[40]. In order to study the direct and indirect effects of each
factor on soil seed bank density, we constructed a structural
equation model using the 'piecewiseSEM' software package.
The SEM model was fitted using the psem function in the
'piecewiseSEM' package, based on generalized least squares,
with the optimal model having the smallest AIC score and a Chi-
Square P-value greater than 0.05[41].
Results
The soil seed bank density (SSBD) of planted forests and
natural forests exhibited significant geographical differences.
The average SSBD of natural forests was 2.876 m−2, ranging
from 1.395 m−2 to 4.049 m−2. In contrast, the average SSBD for
planted forests was 3.137 m−2, ranging from 1.536 m−2 to 3.858
m−2. The difference of SSBD between natural forest and planted
forest is very significant, and the SSBD value of planted forest is
generally higher than that of natural forest (Figure 1b).
Both planted and natural forests showed similar trends in
SSBD in response to changes in climatic factors. SSBD
decreased significantly with increasing temperature and
precipitation (p < 0.001), while it increased with longer sunlight
exposure and higher evaporation rates. Overall, natural forests
exhibited greater climatic plasticity in SSBD (Figure 2).
Compared to planted forests, SSBD in natural forests was
more sensitive to changes in soil nutrients (higher R2). The SSBD
of both forest types increased significantly with the increase of
soil nitrogen content (Figure 3a, b). With the increase of soil
phosphorus content and soil pH, planted forest SSBD showed a
significant decline trend (Figure 3b, c).
SSBD in both forest types showed similar trends in response
to changes in forest stand factors. SSBD in planted and natural
forests positively correlated with forest age and forest DBH, but
negatively correlated with stand density and leaf functional
traits (Figure 4). The impact of forest productivity on natural
forest SSBD (R2 = 0.17) was greater than on planted forests (R2 <
0.01) (Figure 4F). There was a general collinear correlation
between potential influencing factors of SSBD in planted and
natural forests (Figure 5).
All potential influencing factors explained 75.7% of the vari-
ance in SSBD for natural forests and 66.1% for planted forests
(Figure 6). Soil nutrient factors (R2 = 0.361; R2 = 0.377) had a
stronger explanatory power for the spatial variability of SSBD in
both forest types than climatic factors (R2 = 0.301; R2=0.073)
and forest stand factors (R2 = 0.094; R2 = 0.211)(Figure 6). Soil
pH made the largest independent contribution to the spatial
variability of SSBD in natural forests (Figure 6a), while soil nitro-
gen content contributed most significantly to the spatial vari-
ability of SSBD in planted forests (Figure 6b).
Soil pH had the greatest direct impact on SSBD in natural
forests. MAT and MAP not only directly affected SSBD in natural
forests but also indirectly through effects on soil pH, stand
density, and forest NPP, with the direct impacts being greater
than the indirect ones (Figure 7a). For planted forests, soil nitro-
gen content had the greatest direct impact on SSBD. MAE influ-
enced SSBD in planted forests indirectly through its impact on
Na b
1,000 km
2
Natural Planted
Forest type
3
4
SSBD (seed/m2)
SSBD (seed/m2)
2 3 4 Type Natural Planted
Fig. 1 Spatial distribution of soil seed banks and plot locations in planted and natural forests. (a) Comparison of SSBD between natural forests
and planted forests (b) Significance of the differences was assessed using a t-test, with significance at the 0.001 level. ***p < 0.001.
soil seed bank density
Page 4 of 11 Wang et al. Seed Biology 2024, in press
Accepted & Un-edited
soil nitrogen content, with its indirect effect being greater than
the direct effect (Figure 7b).
Discussion
The results of this study show that the SSBD of planted
forests is significantly higher than that of natural forests,
confirming the first hypothesis. Planted forests, characterized
by shorter planting periods and younger ages, tend to have
higher SSBD compared to older, mature natural forests growing
in their natural state[8,42]. In planted forests, the density and
distribution of trees are often carefully planned to maximize
land use efficiency and productivity[43]. Intensive planting will
increase the coverage of vegetation, and after the soil surface is
covered by vegetation, soil erosion and seed loss caused by
erosion can be reduced, which is conducive to the accumulation
4.0
a
3.5
R2 = 0.01, P = 0.005
R2 = 0.13, P < 0.001
SSBD (seed/m2)
3.0
2.5
0 10 20
MAT (℃)
3.5
Natural
Planted
b
2.5
R2 = 0.38, P < 0.001
R2 = 0.07, P < 0.001
1.5
0.5
0 500 1,000 1,500 2,000 2,500
MAP (mm)
4.0
4.5
c
3.5
R2 = 0.27, P < 0.001
R2 = 0.02, P = 0.004
SSBD (seed/m2)
3.0
2.5
2.0
ASD (h)
4.0
Natural
Planted
d
3.5
R2 = 0.09, P < 0.001
R2 < 0.01, P = 0.137
2.5
3.0
2.0
1,000 2,000 3,000 4,0001,000 2,000 2,5001,500 3,000
MAE (mm)
Fig. 2 The relationships between climatic factors and SSBD in natural forests and planted forests. R2 represents the goodness of fit, and P-
values indicate significance. Climatic factors include: (a) Mean Annual Temperature (MAT); (b) Mean Annual Precipitation (MAP); (c) Annual
Sunlight Duration (ASD); (d) Mean Annual Evaporation (MAE).
4.5
a b c
4.0
3.5
SSBD (seed/m2)
3.0
2.5
2.0
0 2
Soil N (g/kg)
4 6
R2 = 0.47, P = 0.001
R2 = 0.49, P = 0.001
3.5
3.0
2.5
2.0
1.5
0 10
Soil P (g/kg)
20
R2 = 0.23, P = 0.001
R2 = 0.02, P = 0.014
3.5
4.0
4.5
3.0
2.5
2.0
4567
Soil pH
8 9
Natural
Planted
R2 = 0.45, P = 0.001
R2 = 0.07, P = 0.249
Fig. 3 The relationships between soil factors and SSBD in natural forests and planted forests. R2 represents the goodness of fit, and P-values
indicate significance. Soil factors include: (a) Soil total nitrogen content (Soil N); (b) Soil total phosphorus content (Soil P); (c) Soil pH (Soil pH).
soil seed bank density
Wang et al. Seed Biology 2024, in press Page 5 of 11
Accepted & Un-edited
and maintenance of seeds in the soil[44]. Moreover, tree species
in planted forests are often selected for high yield or rapid
growth, which may produce higher seed outputs, thereby
increasing the density of the soil seed bank. Planted forests
undergo regular cycles of harvesting and replanting. This peri-
odic human intervention might lead to a regular renewal of
4.0
a
3.5
R2 = 0.04, P = 0.001
R2 = 0.38, P = 0.001
SSBD (seed/m2)
3.0
2.5
0 10050 150 200
Forest age (a)
b
3.5
R2 = 0.03, P = 0.001
R2 = 0.05, P = 0.001
3.0
2.5
0 4020 60
Forest DBH (cm)
c
3.5
3.0
2.5
2.0
500 2,5001,500
Forest density (trees/hm2)
Natural
Planted
R2 = 0.14, P = 0.001
R2 = 0.09, P = 0.001
d
3.5
R2 = 0.02, P = 0.001
R2 = 0.10, P = 0.001
SSBD (seed/m2)
3.0
2.5
0.0−2.5 2.5
Leaf functional traits PC1
eR2 = 0.03, P = 0.001
R2 = 0.01, P = 0.197
3.0
2.5
0 2−2
Leaf functional traits PC2
f
3.5
3.0
2.5
2.0
1.5
0.0 0.5 1.51.0
NPP (kg/m2/a)
Natural
Planted
R2 = 0.17, P = 0.001
R2 = 0.01, P = 0.082
Fig. 4 The relationships between forest stand factors and SSBD in natural forest and planted forests. R2 represents the goodness of fit, and P-
values indicate significance. Forest stand factors include: (a) Forest Age; (b) Forest diameter at breast height (Average DBH); (c) Forest Density;
(d) Leaf functional traits PC1; (e) Leaf functional traits PC2; (f) Net primary productivity (NPP).
Soil pH
Mantel's p
a b
Mantel's p
Mantel's r Mantel's r
Pearson's r
Pearson's r
SSBD
SSBD
0.5
0.8
0.4
0.0
−0.4
0.0
−0.5
<0.01
<0.01
<0.2 <0.2
0.2–0.4 0.2–0.4
≥0.05
≥0.4
Soil P
Soil N
MAP
MAE
MACT
ASD
Forest age
Forest density
Traits PC1
Traits PC2
Soil pH
−0.70
***
−0.22
***
−0.56
***
−0.28
***
−0.39 −0.02
***
−0.24
***
−0.13
−0.06
**
−0.40
***
−0.33
***
−0.14
**
−0.22
***
−0.15
**
−0.19
***
***
−0.12
*
−0.38
***
−0.72
***
−0.17
***
−0.33
***
−0.73
***
−0.31
***
−0.31
***
0.30
***
0.19
***
0.57
***
−0.18
***
0.91
***
−0.12
*
0.30
***
0.56
***
0.25
0.02
−0.02 0.22
***
0.37
0.08
***
0.400.05
***
−0.50
***
−0.17 −0.03
−0.02−0.05−0.07−0.07
**
−0.40
***
−0.74
***
0.16
**
0.52
***
0.56
***
0.21
***
0.90
***
0.22
***
0.48
***
−0.65
***
0.60
***
−0.79
***
−0.60
***
−042 0.62
***
−0.07
***
0.06
−0.08
0.33
***
0.43
***
0.22
***
0.14
***
−0.17
*** *
−0.10
*
*** ******
−0.48−0.35
*** ***
−0.77
***
−0.31
***
−0.32
***
−0.83
***
−0.36
***
0.15
***
0.36
***
0.07 0.17
*** ***
0.11
**
0.68
***
0.180.29
***
0.43
******
0.32
−0.23
−0.31 0.55
***
−0.39
0.09
−0.01−0.34
***
−0.56
***
0.26 −0.21
0.28−0.40−0.29−0.20
***
0.00
*************
−0.69
***
0.51
***
0.69
***
0.69
***
−0.39
***
0.88
***
0.48
***
−0.08
***
Soil P
Soil N
MAP
MAE
MACT
ASD
Forest age
Forest density
Traits PC1
Traits PC2
Soil pH
Soil P
Soil N
MAP
MAE
MACT
ASD
Forest age
Forest density
Traits PC1
Traits PC2
Soil pH
Soil P
Soil N
MAP
MAE
MACT
ASD
Forest age
Forest density
Traits PC1
Traits PC2
Fig. 5 Multivariate correlation analysis of potential influencing factors on SSBD in (a) natural forests and (b) planted forests. MAP:Mean annual
precipitation; MAT:Mean annual temperature; MAE:Mean annual evaporation; MACT:Mean annual coldest month temperature; ASD: Annual
sunlight duration; Soil.N:Soil total nitrogen content; Soil.P:Soil total phosphorus content; Soil.pH:Soil pH; Forest DBH: Forest diameter at breast
height; Traits PC1: Leaf functional traits PC1; Traits PC2: Leaf functional traits PC2.
soil seed bank density
Page 6 of 11 Wang et al. Seed Biology 2024, in press
Accepted & Un-edited
seeds in the seed bank, thereby maintaining or increasing its
density[45]. Compared to natural forests, planted forests gener-
ally harbor (or yield) a large number of light-demanding tree
species with broad ecological niches[46]. These tree species
often produce abundant seeds, and these seeds can persist in
the soil for extended periods.
Numerous studies have shown that climatic factors signifi-
cantly influence the SSBD in forests[13,47,48]. Our experimental
results indicate that the SSBD in both planted and natural
forests decreases with rising Mean Annual Temperature (MAT).
100 Adj. R2 = 0.762
Soil. pH
Soil. P
Soil. N
MAP
MAE
MACT
ASD
Forest age
Forest density
Traits PC1
Traits PC2
Soil
Climate
Forest
−0.6−0.4 −0.2 0.0
Parameter estimates
0.2 0.4 0.6
a
75
50
Relative efect of estimates (%)
25
0
Adj. R2 = 0.685
Soil. pH
Soil. P
Soil. N
MAP
MAE
MACT
ASD
Forest age
Forest density
Traits PC1
Traits PC2
Soil
Climate
Forest
−0.6−0.4 −0.2 0.0
Parameter estimates
0.2 0.4 0.6
b
75
50
Relative efect of estimates (%)
25
0
Fig. 6 Impact of potential factors on SSBD in (a) natural forests and (b) planted forests. The figure presents the average parameter estimates
(standardized regression coefficients), related 95% confidence intervals, and the relative importance of each factor, expressed as the
percentage of explained variance. The adjusted R2 for the average model and the P-values for each predictive factor are denoted as follows: *p
< 0.05; **p < 0.01; ***p < 0.001.
MAT Forest NPP 0.226***
−0.118*
0.393***
−0.278***
Fisher's C = 2.378; P= 0.305; df = 2
−0.167***
−0.440***
−0.440***
−0.07*
−0.748*** 0.591***
0.667***
0.357***
0.381***
a
b
0.426***
R2 = 0.56
Forest density
R2 = 0.37
Soil pH
R2 = 0.62
MAP
MAE Forest NPP −0.08
0.160***
−0.163**
−0.200***
Fisher's C = 2.378; P= 0.305; df = 2
−0.167***
−0.440***
0.669***
0.278***
−0.053 −0.322***
0.689***
0.557***
−0.202***
−0.001
R2 = 0.43
Forest density
R2 = 0.42
Soil N SSBD
R2 = 0.09 R2 = 0.63
SSBD
R2 = 0.63
MAP
Fig. 7 Relationships between SSBD and climatic factors, soil nutrients, and forest stand factors in (a) natural forests and (b) planted forests.
The path diagrams represent the standardized results of the final Structural Equation Models (SEMs) testing relationships between variables.
Numbers alongside the paths indicate the standardized SEM coefficients, and asterisks denote significance (***p < 0.001; **p < 0.01; *p < 0.05).
R2 indicates the goodness of fit for the generalized additive models. The best SEMs were selected based on the lowest Akaike information
criterion.
soil seed bank density
Wang et al. Seed Biology 2024, in press Page 7 of 11
Accepted & Un-edited
Research suggests that temperature is a key climatic factor
affecting seed dormancy and stimulating germination[49]. Cold
conditions slow down the metabolic rate of seed embryos and
germination rates. Seeds that grow in colder regions tend to
have higher longevity and survival rates compared to those in
warmer regions[50]. As MAT increases, seed germination rates
rise, while seed vitality and persistence decrease. Studies also
show a positive correlation between temperature and the
frequency of predator activities; higher MAT can increase the
predation rate of germinated seeds in the soil[19]. Our results
demonstrate that SSBD in planted forests is more sensitive to
temperature changes than in natural forests (Figure2). This
could be due to the forest climate formed in natural forests
over time[51]. Natural forests have more developed ecosystems
and a stronger resistance to environmental changes, making
their SSA less sensitive to increases in MAT compared to
planted forests[9]. Therefore, the response of natural forests to
MAT rise in SSBD is less sensitive than that of plantation forests.
Research shows that SSBD significantly decreases with
increased precipitation, consistent with our findings[48].
Increased rainfall can break seed dormancy and stimulate
germination. However, early germination is not conducive to
seed growth; changes in rainfall affect the longevity of the seed
bank, and increased Mean Annual Precipitation (MAP) directly
impacts the risk dispersal mechanisms of seeds, potentially
causing a decrease in SSBD[13,48]. Our results show that SSBD in
natural forests is more sensitive to MAP compared to planted
forests, possibly because planted forests, due to artificial irriga-
tion, have less water demand. In contrast, natural forests are
often in a state of drought and water scarcity, making their soil
seed banks more responsive to rainfall compared to those in
planted forests[52]. In our study, other climatic factors also affect
the SSBD of planted and natural forests, but according to the
results of the comprehensive structural equation model, MAT
and MAP are the key climatic factors affecting SSBD in both
planted and natural forests.
The experimental results of this study show that there is a
close relationship between the SSBD in planted and natural
forests and soil nitrogen content, phosphorus content, as well
as soil pH. Being in wild and impoverished soils, natural forests
are limited by soil nitrogen nutrients, while planted forests,
under artificial cultivation, still require timely nitrogen fertiliza-
tion to ensure normal tree growth[53]. Both planted and natural
forests are limited by nitrogen in their soil environments. The
development of forests in China is primarily limited by nitrogen
elements[54]. Therefore, an increase in soil nitrogen content is
conducive to the growth and development of germinating
seeds in the soil[55]. Our results show that SSBD in both planted
and natural forests is positively correlated with soil nitrogen
content. Compared to the limitation of soil nitrogen content on
planted and natural forests, the limitation of soil phosphorus
content is not very strong. Chen et al. (2022) have shown that
seed vigor in the soil seed bank is positively correlated with soil
available P content, which also explains the experimental
results of this study[56]. In planted and natural forests, seed
vigor is positively correlated with soil total phosphorus content.
Higher seed vigor in soil seeds changes their bet-hedging ability
and risk dispersal strategies, increasing their risk of extinction.
Therefore, SSBD tends to be lower in environments with higher
soil phosphorus content[57]. Seed germination in acidic soils is
limited, and as soil pH gradually changes from acidic to neutral,
plant efficiency in utilizing soil nutrients increases[58]. In natural
forests, an increase in soil pH improved the nutrient uptake effi-
ciency of seeds in the soil and significantly increased SSBD. In
planted forests, however, there is no significant linear relation-
ship between SSBD and soil pH, possibly because the soil pH in
planted forests, due to artificial afforestation, is mostly
neutral[59].
In both planted and natural forests, older forests with larger
average diameters at breast height (DBH) typically have longer
successional periods[9]. As forests age, the ecosystem gradually
evolves towards a more mature state, during which the number
of seeds usually increases[27]. In the later stages of succession,
interspecific competition among forest trees diminishes,
resources shift towards reproduction, and more seeds are
produced[60]. Additionally, due to prolonged seed deposition,
the soil seed bank gradually accumulates more seeds. There-
fore, SSBD shows a positive correlation with forest age and
average DBH[27]. Experimental results indicate that forest stand
density is significantly negatively correlated with SSBD. Forests
with higher stand density have higher canopy closure, resulting
in less light reaching the understory vegetation and soil seed
germination, making nutrient uptake more difficult and lower-
ing SSBD[61]. Studies have shown that leaf functional traits such
as LA,SLA,LDMC,LN,LP, etc. can affect soil structure and nutrient
cycling under the influence of leaf litter, thus disturbing soil
seed bank density changes. The effect of leaf functional traits
on soil seed bank density in natural and planted forest commu-
nities was driven by multi-dimensional traits rather than single
traits. SSBD in both planted and natural forests decreases with
an increase in leaf functional traits PC1 and PC2, indicating a
consistent response of SSBD in planted and natural forests to
changes in leaf functional traits. Numerous studies have shown
that key leaf traits can effectively predict the productivity of
forest communities[3,35,62]. In communities with higher produc-
tivity, trees allocate more resources to growth and development
and engage in greater interspecific competition. Consequently,
trees that reduce their own reproduction result in fewer seeds
produced by trees, resulting in lower SSBD[61,63,64].
Variance decomposition results indicate that, compared to
climatic and forest stand factors, soil factors are the primary
drivers affecting the SSBD in both planted and natural forests.
This finding contradicts Hypothesis 2. Nutrients in the soil
directly influence the germination and growth of soil seeds,
having a more direct and intense impact than climatic factors,
consistent with predictions by Yang et al. (2021) regarding
global soil seed bank density influencers[2]. This study also
found that among the biotic and abiotic factors affecting SSBD,
soil pH is the most significant factor for natural forests, while
soil nitrogen content is the most significant for planted forests.
Similar results were found in Ma et al.’s (2020) study of the herb
layer seed bank on the Qinghai-Tibet Plateau[13]. Increased soil
pH enhances seed persistence, and soil pH might be indirectly
influenced by precipitation, affecting SSBD in natural forests.
Nitrogen, one of the most limiting factors for plant growth in
terrestrial ecosystems, plays a key role in influencing seed
germination and growth. In planted forest ecosystems, which
are generally low in nitrogen, growth is limited by nitrogen
availability[53]. Acidic soils may affect seed size, lifespan, and
vigor, and increased nitrogen content benefits plant carbon
storage and promotes the accumulation of soil organic
matter[65]. Therefore, the nitrogen content in planted forests
soil seed bank density
Page 8 of 11 Wang et al. Seed Biology 2024, in press
Accepted & Un-edited
impacts soil nutrients, and increasing nitrogen availability can
alter community structure and composition. Increasing the
availability of nitrogen can increase the richness of vegetation
in the above-ground herbaceous layer, accelerate the growth
and propagation of trees, and increase SSBD[66].
Gong et al. (2023) found that the interaction between
climatic and soil factors significantly affects the ecosystem
functions of planted and natural forests[3]. An et al. (2020) also
discovered in their study of the soil seed bank on the Qinghai-
Tibet Plateau that climatic changes affect SSBD by influencing
above-ground community structure and soil nutrient availability
[48]. This study also found that climatic, soil, and forest stand
factors not only have a direct impact on SSBD but also that
climatic factors indirectly affect SSBD in planted and natural
forests by influencing forest community succession and soil
nutrient availability, confirming Hypothesis 3. Rising tempera-
tures accelerate microbial activity in soil, increasing the decom-
position rate of organic substances like nitrogen and phospho-
rus, making more nutrients available for soil seeds[67]. Higher
temperatures also increase community productivity, promote
tree growth and development, increase forest canopy closure,
reduce light available to understory vegetation, and decrease
the richness and density of the soil seed bank[68]. Increased
precipitation, on one hand, raises soil moisture and water
content, increasing pathogens around soil seeds, reducing seed
vigor and density[13]. On the other hand, increased precipitation
limits nutrient transport in plant roots and restricts nitrogen
mineralization in soil, reducing nutrients available for seed
absorption[67]. Studies have found that precipitation and tree
layer productivity are positively correlated; increased precipita-
tion promotes forest tree growth. Trees adopt growth strategies
over reproductive strategies with increased rainfall, reducing
seed production. Additionally, tree growth increases forest
canopy closure, reducing the light required for seed germination
[46], thereby affecting SSBD.
Author contributions
The authors confirm contribution to the paper as follows:
study conception and design: Gao J, Guo X; data analysis: Wang
J, Wang R, Zhang X, Xu J, Zhang X; draft manuscript prepara-
tion: Wang J, Guo X, Gao J; manuscript revision: Wang J, Gao J.
All authors contributed to the discussion of results, manuscript
preparation, and approved the final version.
Data availability
All data generated or analyzed during this study are included
in this published article and its supplementary information files.
Acknowledgments
This work was supported by the Xinjiang Normal University
Young Top Talent Project (No.XJNUQB2023-14), Natural Science
Foundation of Xinjiang Uygur Autonomous Region
(No.2022D01A213), Fundamental Research Funds for Universi-
ties in Xinjiang (No.XJEDU2023P071), National Natural Science
Foundation of China (No.32201543), Innovation and
Entrepreneurship Training Program for College Students in
2023 (No.S202310762004), Xinjiang Normal University Land-
mark Achievements Cultivation Project (No.XJNUBS2301),
Xinjiang Graduate Innovation and Entrepreneurship Project
and Tianchi Talent Program.
Conflict of interest
The authors declare that they have no conflict of interest.
Dates
Received 16 August 2024; Revised 16 October 2024;
Accepted 12 November 2024; In press 21 November 2024
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Copyright: © 2024 by the author(s). Published by
Maximum Academic Press on behalf of Hainan
Yazhou Bay Seed Laboratory. This article is an open access article
distributed under Creative Commons Attribution License (CC BY
4.0), visit https://creativecommons.org/licenses/by/4.0/.
soil seed bank density
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