Material and methods
Data collection
We searched for all peer-reviewed publications that investigated the
effects of plant diversity on carbon and nitrogen attributes in
grasslands using the ISI Web of Science (isiknowledge.com) and China
National Knowledge Infrastructure (CNKI, www.cnki.net) databases from
1980-2019. Keyword combinations such as diversity/mixture,
grassland/pasture/meadow, carbon/C, nitrogen/N, respiration, and
richness were used for the search. The following criteria were applied
to select studies: 1) experiments had at least one pair of data
comparing monoculture vs. mixture, 2) the species in monoculture were
included in the mixtures at the same temporal and spatial scale, 3)
response variables (means and measures of variance) were reported for at
least one carbon or nitrogen ecosystem function: aboveground biomass
(AGB), belowground biomass (BGB), total biomass (TB), soil carbon pool
(SCP, %), soil respiration (Rs), heterotrophic respiration (Rh),
microbial biomass (MB), bacterial biomass (BB), fungal biomass (FB),
aboveground nitrogen pool (ANP), soil nitrogen pool (SNP, %), soil
ammonium nitrogen (SAN), soil nitrate nitrogen (SNN), soil nitrogen
leaching (SNL), soil nitrogen mineralization (SNM), and 4) the diversity
level (species richness), experimental duration, and experimental type
(field or greenhouse) were clearly described. Means, standard
deviations/errors and samples size were extracted when necessary from
digitized graphs using GetData Graph Digitizer (ver. 2.24, <
www.getdata-graph-digitizer.com/ >). Sample sizes
corresponding to each observation was derived based on the number of
independent experimental units. The data were extracted from the
Worldclim database at http://worldclim.org/ using the location
information (e.g. latitude and longitude). Experimental age was divided
into two durations: 1 (1– 4 years) and 4 (>4 years). In
total, 73 studies, reporting 15 attributes, and 1385 observations about
the effects of plant diversity on carbon and nitrogen processes were
selected (Table S1).
Data analysis
We followed the methods of Hedges et al.
(1999) and Gurevitch et al. (2018) to
evaluate the changes in carbon and nitrogen attributes in plant
mixtures. The log response ratio (lnRR , natural log of the ratio
of the mean value of monocultures plots to that in mixtures) was
calculated as below:
\(lnRR=\ln\left(\frac{{\overset{\overline{}}{x}}_{t}}{{\overset{\overline{}}{x}}_{c}}\right)=\ln\left({\overset{\overline{}}{x}}_{t}\right)-ln({x\overline{}}_{c})\)(1)
where x̅t and x̅c are means of the
variable in all mixtures and monocultures plots respectively.
Random-effects models were used to estimate weighted average response
ratios via the rma function in the metafor package (Cooperet al. 2009) in R 3.5.1 (R Core Team, 2018). Weights for studies
were estimates by sampling variance (Hedges et al. 1999) and the
between-sampling variability:
\(w_{v}={(\frac{s_{t}^{2}}{n_{t}{\overset{\overline{}}{x}}_{t}^{2}}+\frac{s_{c}^{2}}{n_{c}{\overset{\overline{}}{x}}_{c}^{2}}+\tau^{2})}^{-1}\)(2)
where St, nt, Sc and
nc are the standard deviation and sample size for the
mixtures and monocultures respectively, and \(\tau^{2}\) the total
amount of heterogeneity.
For each attribute, we tested the impacts of species richness (SR),
experimental age (EA), and climate using the following model:
lnRR = β0 + β1 × lnSR+
β2 × EA +β3 × climate
+β4 × lnSR ×EA+β5 × lnSR ×climate +ɛ (3)
where β is the coefficient to be estimated; ɛ is sampling error.
We conducted the analyses using restricted maximum likelihood estimation
by the rma function with \(w_{v}\) as the weight for each
corresponding observation (Johnson and Omland 2004; Bates et al.2015). For ease of interpretation, lnRR and the corresponding
95% confidence intervals (CIs) were transformed to a percentage change
between monocultures and mixtures as (e lnRR-1) ×100%. To illustrate the effects of plant diversity on soil carbon
and nitrogen pool with time, we compared the lnRR when the plant
diversity in mixtures was R1 (all species present) and
Rα (α% lower species richness) using the following
equation (Chen et al . 2019):
\(P_{\alpha}={{(R}_{1}/R_{\alpha})}^{\beta_{1}+\beta_{4}T}\) (4)
where Pα is the proportion of remaining soil
carbon and nitrogen pool under α% lower species richness in a period ofT. Other model terms were as described for Equation (3). Based on
Equation (4), we fitted curves to estimate the potential decrease in
soil carbon and nitrogen pool over time under scenarios of 10, 20, 40,
and 80% decreases in species richness. We limited the forecasting to 30
years as that is the age of the longest field biodiversity experiment at
Cedar Creek Natural History Area, Minnesota, USA (Tilman et al.2006).