Abstract
The diversity–productivity
relationship in grasslands is predominantly positive but also highly
variable because of its complex influencing mechanisms in natural
ecosystems. In this study, we investigated plant diversity, biomass, and
associated drivers (e.g., climate, soil, and plant traits) along an
elevational gradient in grasslands in southwest China. Grassland biomass
decreased significantly, but grassland diversity increased with
increasing elevation. Consequently, a significant negative relationship
between grassland biomass and diversity was detected along the
elevational gradient. We also
observed that the negative relationship was primarily driven by climatic
factors (i.e., temperature and precipitation) and plant stoichiometric
traits (i.e., phosphorus limitation) rather than by soil properties at a
regional scale. This is inconsistent
with previous studies on the positive diversity–productivity
relationship, which might weaken the effects of climatic factors at the
regional scale. Our results revealed
that the negative relationship between diversity and productivity in
grasslands was shaped by the combined effects (climate and plants) on
productivity and diversity in grasslands.
Keywords: biomass, climates, diversity, elevational gradient,
grasslands, stoichiometry.
Introduction
The diversity–productivity
relationship is fundamental for understanding and predicting the impact
of biodiversity on ecosystem functions and services (Isbell et
al. 2011; Fraser et al. 2015; Pan et al. 2022).
Diversity–productivity
relationships have been studied globally across various ecosystems,
including tropical forests (Poorter et al. 2015), temperate
forests (Chisholm et al. 2013), shrubs (Chen et al. 2018),
and grasslands (Ladouceur et al. 2022). These studies propose a
positive diversity–productivity relationship predominant in plant
communities worldwide, implying that high diversity could promote
productivity. However, the diversity–productivity relationships in
grasslands show divergent patterns, including positive linear (Baiet al. 2007), positive nonlinear (Craven et al. 2016),
unimodal (Fraser et al. 2015; Wang et al. 2022), and
neutral patterns (Adler et al. 2011). Thus, understanding the
realistic relationships and
mechanisms underlying these observed divergent patterns is important for
accurately predicting how diversity influences productivity in natural
ecosystems under environmental changes.
Inconsistent diversity–productivity relationships are mainly determined
jointly and/or solely by a wide array of drivers, including climate,
plants, and soil properties (Hawkins et al. 2003; Hautieret al. 2009; Grace et al. 2016; Kimmel et al.2020). Rising temperatures can alter species interactions leading to
biodiversity loss and improper ecosystem functioning (García et
al. 2018). Precipitation also mediates the strength of the
diversity–productivity relationship by changing water availability and
soil moisture in steppe grasslands (Hossain & Beierkuhnlein 2018; Liet al. 2020). Changes in environmental factors (elevation,
temperature, and precipitation) can increase environmental
heterogeneity, which modulates the effects of plant diversity on the
productivity variability of grasslands (Stein et al. 2014; Daleoet al. 2023). Productivity and biodiversity in grasslands are
also mediated by soil properties, such as soil aridity, pH, and chemical
constituency, which can affect the diversity–productivity
relationships, likely by changing plant growth parameters and species
composition (Ceulemans et al. 2013; Palpurina et al. 2017;
Kimmel et al. 2020). Furthermore, several experiments on nitrogen
(N) addition to grasslands have indicated that soil nutrient enrichment
significantly increases community biomass but decreases community
diversity (Hautier et al. 2009; Harpole et al. 2016;
Seabloom et al. 2021).
Plant stoichiometry usually modulates nutrient limitation and
utilization for plant growth (Allen & Gillooly 2009; Sperfeld et
al. 2017). Plant nitrogen, which is a protein component, plays a role
in plant biomass production and litter decomposition (Yang et al.2018; Yu et al. 2020), whereas phosphorus (P) is coupled with C
and N and linked to biological processes such as photosynthesis and
growth (Delgado-Baquerizo et al. 2013; He et al. 2020).
Plant stoichiometry is associated with soil stoichiometry, which
directly regulates plant biomass production by changing soil nutrient
availability, and hence indirectly mediates plant community diversity by
adjusting plant interspecific interactions (e.g., plant coexistence and
competition) (Striebel et al. 2009; Ning et al. 2021;
Gerhard et al. 2022). Moreover, climatic factors affect the
diversity–productivity relationships by indirectly controlling plant
stoichiometry(Dijkstra et al. 2012; Yu et al. 2015; Qinet al. 2022). Some studies have shown that foliar N and P content
generally decreases with increasing mean annual temperatures and
precipitation (Tang et al. 2018). However, the stoichiometric
responses to climate differ substantially between plant species. For
instance, a global analysis reported that foliar N and P in birch(Betula) increased, but foliar N and P in grass(Calamagrostis) decreased with increasing temperature (Reich &
Oleksyn 2004). In addition, some studies have reported that climate has
a weak effect on plant stoichiometry, but there is a significant
elevational trend with the C:N and C:P ratios decreasing with elevation
(Yang et al. 2015). While most studies on the
diversity–productivity relationship have focused on one or two specific
variables, they ignored the intermixed effects of climate, soil, and
plants.
Southwestern China is regarded as a global biodiversity hotspot because
of its distinct environmental gradients and complex geomorphological
features along its elevational gradients (Rahbek et al. 2019).
The high diversity and diversity–productivity relationships of
grasslands in this region are more sensitive to climate along
elevational gradients (Mumbanza et al. 2021b). Elevational
gradients are considered open-air laboratories where we can investigate
natural ecosystem responses to long-term climate change (Malhi et
al. 2010). Thus, a survey of diversity–productivity relationships in
grasslands along elevational gradients could assist in evaluating the
underlying mechanisms of plant growth, soil nutrients, and their
interactions under changing climatic conditions (Mumbanza et al.2021b). Here, we explored the patterns of diversity–productivity
relationships and associated drivers (i.e., climatic factors, soil
properties, and plant traits) based on intensive field data collected
along an elevational gradient in the grasslands of southwest China. Our
study was designed to address the following questions: (a) How do plant
diversity, biomass, and their relationships in grasslands vary with
elevational gradient? (b) How do abiotic factors (i.e., climatic factors
and soil properties) and biotic factors (plant nutrients) jointly and/or
solely affect the diversity and biomass of grasslands? We hypothesized
that plant diversity and grassland biomass would increase with
increasing elevation, possibly due to changes in temperature and
precipitation, and that a positive relationship between diversity and
productivity would be observed along climatic gradients. We hypothesized
that plant and soil factors would have a significant positive impact on
diversity and productivity, owing to nutrient (N and P) resource
limitations, despite the effects of climate change (Striebel et
al. 2009; Yu et al. 2020).
Methods and materials
Study area and field sampling
We set up a grassland transect (≈1,000 km length) across southwest China
along an elevational gradient range from 40–3800 m
(Fig 1). The mean annual
temperature (MAT) ranged from 7.6–23.6 °C, mean annual precipitation
(MAP) ranged from 730–1760 mm. MAT and MAP were closely related to
elevational gradients (MAT, R2 = 0.74,
p <0 .001; MAP, R2 = 0.28,
p <0 .001; Fig S1). The main vegetation types along the
grassland transect were subtropical and temperate montane. Soil types
mainly consist of Acrisols, Luvisols, and Cambisols (World Reference
Base 2006).
During the 2021 growing season, 98 field sites were surveyed along the
transect. For each site, a 30 m × 30 m quadrat was constructed, inside
which six sub-plots (1 m × 1 m) were selected and investigated in the
center and four corners (only three 1 m × 1 m plots in the quadrat for
some sites). In total, 546 plots were investigated. The geographical
coordinates and elevation of each site were recorded using a global
positioning system (GPS).
Plant biomass, diversity, and plant traits
All species in the plots were recorded, and all ground-level plants were
grouped by species and were harvested and stored in the envelopes.
Aboveground vegetation was weighed using live biomass and then
oven-dried to a constant weight at 65 °C to weigh the dry biomass for
further analyses. Two or three dominant plant species were selected from
each site and sealed in a paper envelope to measure the foliar element
content. The Shannon-Wiener index, Simpson index, and species richness
(SR) were calculated as measures of community biodiversity at the plot
and site scales. Species richness (number of
species/m2) at each site was calculated as the average
number of species in each plot. The community biodiversity index was
calculated by the “vegan” package
in R (Dixon 2003). Three topsoil samples (0–10 cm depth) were collected
with a 7 cm auger in each site,
then sealed in plastic bags and
stored at -20℃ for further analysis.
Leaf C content, N content, and δ 13C were
measured using an isotope ratio mass spectrometer (Delta V Advantage,
Thermo Fisher Scientific, Waltham, MA, USA) connected to an elemental
analyzer (Flash 2000 EA-HT, Thermo Fisher Scientific). Leaf P content
was determined using the molybdate/stannous chloride method after
H2SO4–H2O2digestion (Kuo 1996), and the extraction solution was analyzed using an
automated discrete analyzer (DeChem-Tech GmnH Inc. Hamburg, Germany).
Soil properties
Soil samples were air-dried and homogenized using a 2 mm sieve, and the
visible roots and small stones were removed for further measurements.
Soil organic carbon (SOC) and soil total nitrogen (STN) were determined
using an elemental analyzer (Vario EL, Elementar Analysensysteme GmbH,
Langenselbold, Germany) after the removal of soil carbonates using 1N
HCl for 24 h. Bulk density was determined using 5-cm-diameter soil
cores. Soil pH and clay content were extracted from a global compilation
of the soil profile database
(https://soilgrids.org/). The
dominant herbaceous leaves were oven-dried at 60 ºC for 48 h to achieve
constant weight, after which they were weighed and ground using an
ultra-centrifugal mill (JXFSTPRP-32, JingXin, China).
Climatic data collection
Mean annual precipitation (MAP, mm) and mean annual temperature (MAT,
°C) data at a resolution of 30 arcseconds were obtained from the
WorldClim database
(http://www.worldclim.org).
Elevation data were extracted from the shuttle radar topography mission
database (SRTM)
(https://srtm.csi.cgiar.org)
with a 30-arcsecond resolution based on the geographical coordinates of
the sites. The extracted analysis was performed using the “geodata” R
package.
Statistical analysis
Linear regression models generally explore the relationships between
climatic factors, community diversity, and biomass. Linear and quadratic
functions were fitted to the diversity–productivity relationships
across plots and site levels. Model comparisons were performed based on
the explained variance and the significance of the corresponding
coefficients (slope and quadratic terms). Variation partitioning
analysis (VPA) was conducted to test the contribution of climates,
plants, and soils to diversity and biomass using the “vegan” package
in R. The Random Forest (RF) algorithm using the “randomForest”
package, was used to detect the relative importance of predictors
selected to explain variations in the diversity and biomass. One-way
analysis of variance (ANOVA) and Tukey’s honest significant difference
(HSD) multiple comparison test (p<0.05) were used to evaluate
the statistical significance of diversity, biomass, and leaf
stoichiometric properties at different elevations. Structural equation
modeling (SEM) using the “piecewiseSEM” package was used to clarify
the direct and indirect effects of climates, soils, and plants on the
community biomass and diversity by. All statistical analyses were
performed using R software v.4.2.1 (R Core Team, Vienna, Austria; URL:
https://www.Rproject.org/).
Results
Variations in biomass and diversity along climatic gradients
Grassland biomass decreased significantly (p<0.001) with
increasing elevation (Fig. 2 a), whereas it increased with increasing
MAT and MAP (Fig. 2 b, c). For plant diversity, the Shannon diversity
index significantly increased with elevation and decreased with MAT
(p<0.001) (Fig. 2 d, e);
however, there was no significant change with MAP (p>0.05)
(Fig. 2f). We detected a negative relationship between grassland biomass
and plant diversity, including the Shannon diversity index (slope =
-210.67, p<0.01), Simpson diversity index (slope=-493.00,
p<0.01), and species richness (slope = -48.33,
p<0.01) (Fig. 2 g-i). However, we observed that both delta
biomass and delta elevation were significantly and positively correlated
with Bray–Curtis dissimilarity values (β diversity,
p < 0.001) (Fig. 3 a, b)
Shifts in plant and soil
stoichiometric traits along
elevational gradients
Along the transects, grass biomass responded differently to increasing
elevations in terms of foliar and soil stoichiometric traits. In
particular, no significant correlations of leaf C and P content with
elevational gradients were found (Fig. 4 a, c), whereas leaf N content
exhibited a notable positive trend with increasing elevation
(p < 0.001; Fig. 4 b). The leaf C:N ratio decreased with
increasing elevation (slope=-0.003, p<0.001; Fig. S2a). In
addition, relatively weak upward and downward trends were observed in
the leaf N:P and leaf C:P ratios, respectively. (Fig. S2 b, c). The leaf
C:N ratio showed a significantly positive relationship with biomass, and
leaf C:P and N:P ratios were strongly negatively correlated with biomass
(Fig. 4 d, e, f), and in contrast to the weak effect of elevation on
plants, both soil C and N exhibited extensive upward trends with
increasing elevation (p<0.001; Fig. S3 a, b),
whereas there was no significant
effect of elevation on the soil C:N ratio (Fig. S3 c).
Drivers of diversity–productivity relationships in grasslands
Variation partitioning analysis (VPA) evaluated the contributions of
climate, plants, and soil to grassland biomass and diversity (Fig. 5).
Climatic variables explained the greatest variation in biomass (16%),
followed by plants (10%), soils/climates (5%), and climates/plants
(4%) (Fig. 5a). Similarly, climatic
variables were the most important factors, explaining 11% of the
grassland diversity (Fig. 5b). It was observed that the plant biomass
positively correlated with the MAT, MAP, and leaf P contents, but
negatively correlated to the elevation, leaf N:P ratio (Fig. S5). In
contrast, plant diversity was negatively correlated with MAT and leaf
N:P and positively correlated with elevation, MAP, and leaf P content
(Fig. S5).
The RF algorithm further revealed that climatic factors such as
elevation, MAT, and MAP and plant variables such as leaf P and diversity
were important drivers of biomass with a relative importance of 16.01%,
13.31%, 11.14%, 9.83%, and 8.77%, respectively (Fig. 6 a). With
respect to plant diversity, elevation, MAT, BD, MAP, and STN were the
most important drivers accounting for 10.64%, 10.51%, 9.81%, 7.76%,
and 6.63% of relative importance, respectively (Fig. 6 b). SEM analysis
also indicated that climatic gradients were key factors constraining
both grassland biomass and diversity, with direct/indirect effects on
the grass community and relevant soil and/or plant properties (Fig. 7).
Discussion
In this study, we investigated the relationships between grass
diversity, aboveground biomass, and associated drivers based on
intensive field plot datasets along an elevational gradient in the
grasslands of southwestern China.
Our results showed a negative
relationship between grassland biomass and plant diversity along the
elevational gradient, which was in
contrast to our hypothesis, and the prevailing view showed that plant
diversity tends to exert a positive effect on plant biomass (Graceet al. 2016; Chen et al. 2018). We found that the negative
diversity–productivity relationship in grasslands was largely
constrained by the climate and stoichiometry in the study region.
Notable climatic gradients (MAT and MAP) were observed in the field plot
dataset (Fig S1). We found a significant negative relationship between
elevation and biomass and a positive correlation between MAT, MAP, and
biomass (Fig 2). We detected opposite patterns for plant diversity,
showing that diversity was positively related to elevation and
negatively related to MAT and MAP (Fig 2). These results suggest that
the impact of diversity on community biomass is a response to
environmental stress and that plant diversity and biomass are
simultaneously regulated by
environmental factors (Loreauet al. 2001; Steudel et al. 2012; Wang et al.2022). Furthermore, we found that delta biomass and delta elevation were
significantly positively correlated with Bray–Curtis dissimilarity
values (β diversity, Fig 3), implying that the environmental
heterogeneity caused by climatic gradients might be the key factor in
explaining the variability in biomass and diversity (Stein et al.2014; Qiao et al. 2022; Daleo et al. 2023). For instance,
we found higher diversity but lower biomass in the highlands owing to
environmental heterogeneity caused by notable changes in elevation and
environmental stress limited by lower temperature and precipitation (Fig
8). This evidence further underpins that the relationship between
diversity and grassland biomass is strongly controlled by environmental
gradients (Lundholm 2009; Daleo et al. 2023).
Another possible explanation for our results is that shifts in
ecological stoichiometric
characteristics along elevational gradients may also affect the
relationship between diversity and community biomass. We found that
plant stoichiometric factors were the second most important variable
explaining the variation in biomass production (Fig 5,6), supported by
the significantly positive relationships of leaf C:N with biomass and
the negative correlations of leaf C:P and leaf N:P with biomass (Fig 4).
The stoichiometry variation could result from nutrient limitation and
nutrient-use strategies along elevational gradients (Harpole et
al. 2011; Palpurina et al. 2019). For example, lowlands are
usually regarded as high-resource habitats because of their higher
temperature, precipitation, and nutrient supply (N and P), which can
facilitate the rapid growth of one or two species and contribute to
major community productivity. A reverse pattern (high diversity but low
biomass) was detected in the highlands, which could result from low
resource limitations (e.g., P limitation). In the present study, leaf N
content exhibited a notable upward trend with increasing elevation, but
there was no remarkable variation in leaf P content, leading to an
increase in leaf N:P with increasing elevation (Fig 8). This finding
agrees well with previous studies showing that leaf N content increased
with elevation, and lower leaf N:P was present in high-elevation areas
(Mumbanza et al. 2021a; Qin et al. 2022). Typically, a
higher leaf N:P ratio would reduce plant growth rate and biomass
production because a higher growth rate requires more ribosomes and
proteins; thus, plants need to produce more ribosomal ribonucleic acid
(rRNA) by maintaining a higher P content and lower N:P (Elser et
al. 1996; Sardans & Peñuelas 2013). Therefore, P limitation is one of
the key drivers determining the diversity-biomass relationship in the
highlands.
We also found that the effect of diversity on productivity in grasslands
was influenced mainly by climatic factors and plant stoichiometry rather
than by soil properties (Fig 5,7). This finding suggests that the effect
of diversity on productivity changes in response to environmental
gradients and that the diversity–productivity relationships are
non-uniform because they are easily offset by co-varying environmental
factors (Adler et al. 2011; Li et al. 2020). Many previous
studies have reported that patterns of diversity and productivity are
dominated by spatial climatic variations (Seddon et al. 2016;
Jiao et al. 2017). For instance, some studies have verified that
MAP is a crucial factor regulating aboveground biomass (Guo et
al. 2012; Hossain & Beierkuhnlein 2018), whereas others have shown
that the combined effects of MAT, MAP, and resource availability,
predominate the patterns of diversity and productivity (Grace et
al. 2016; Li et al. 2020; Wang et al. 2022). However,
some studies also pose challenges, showing that diversity continues to
have a stronger impact on biomass, outperforming other factors such as
climate and nutrient availability (Wu et al. 2016; Duffy et
al. 2017). The inconsistent diversity–productivity relationships could
be attributed to research scales and methods. At a community scale or in
a control experiment, species interaction may be the principal force for
the diversity–productivity relationships and a certain positive
diversity–productivity relationship is often observed due to species
interaction (e.g., complementarity effect and insurance effect)
(Cardinale et al. 2007; Isbell et al. 2009). At a regional
scale or a natural observation, environmental variables would be the key
drivers for the patterns of diversity and productivity (Chisholmet al. 2013; Li et al. 2020). Community-scale manipulative
experiments can easily reflect the diversity effect in a plant
community owing to certain
experimental conditions, whereas the factors that control the
diversity–productivity relationships are more complicated at a regional
scale (e.g., spatial heterogeneity, human disturbances, and nutrient
availability). Therefore, research scales must be considered when
explaining inconsistent diversity–productivity relationships, and both
experimental and observational investigations must be conducted.
Conclusion
In this study, we explored the relationship between grass diversity and
aboveground biomass and the effects of environmental factors on these
interactions using data from intensive field plots along an elevational
gradient. Our results showed a significant negative relationship between
diversity and biomass along an elevational gradient. These negative
relationships were mainly driven by climatic factors and plant
stoichiometric traits, implying that
climatic factors and nutrient limitations caused by environmental
gradients determine patterns of diversity and productivity.
These results suggest that
previous controversies regarding the
relationship between diversity
and productivity may weaken the impact of climatic factors at regional
scales, thus providing new insights into the long-standing debate over
the relationship between diversity and productivity. These results would
also provide a better understanding of the joint effects of climatic
factors, soil properties, and plant nutrient limitations on productivity
and diversity in grasslands under climate change.