How and to what extent have degraded grasslands recovered after ecological restoration in China: A meta-analysis? - Su, Jishuai; Xu, Fengwei; Zhao, Yujin; Wang, Yang; Bai, Yongfei (Dryad, 2021)
A range of measures for restoration have been tested on degraded
grasslands in China. However, the factors controlling recovery to natural
grassland conditions and the overall effectiveness of restoration remain
unclear. We synthesized data from 365 studies for 103 grassland sites,
with 21 variables related to plant community and soil properties. We used
meta-analysis to evaluate the effectiveness of ecological restoration and
the influence of restoration approach, grassland type, soil depth,
climatic conditions, and the duration of restoration programmes.
Ecological restoration showed larger enhancements on plant community
structure and biomass compared with plant diversity and soil properties,
and the recovery of soil properties was substantially slower than the
recovery of vegetation. Specifically, aboveground biomass showed faster
recovery than belowground biomass, but plant species richness did not
achieve full recovery to natural conditions. Soil water content, soil
carbon and nitrogen content, and soil carbon storage did not recover fully
to natural conditions, although they were improved to a greater extent in
topsoil than in subsoil. In contrast, soil nutrient availability and
microbial biomass generally recovered to the level of natural grasslands,
and their restoration efforts tended to increase with soil depth, possibly
due to the more enhanced belowground biomass in subsoil. There was no
general significant difference in restoration efforts between passive and
active restorations. The duration of restoration programmes, grassland
type, and climatic conditions modulated the effectiveness of grassland
restoration. Synthesis and applications. Our study showed that vegetation
recovered faster than soil in degraded grasslands after ecological
restoration. Given that passive and active restoration regimes achieved
similar recovery, we recommend that passive restoration as the most
cost-effective option for restoring degraded grasslands where a
spontaneous recovery process is possible. We also propose that a
restoration network is initiated to facilitate the development of
grassland recovery strategies, informed by restoration approaches,
grassland types, and climatic conditions.
Data compilation of restoration studies in degraded grasslands The ISI Web
of Science (http://apps.webofknowledge.com/) and the China National
Knowledge Infrastructure (CNKI) institution were searched for
peer-reviewed literatures before 6 July 2020, using the following search
term combinations: (grassland OR steppe OR meadow OR pasture OR rangeland)
AND (restor* OR rehabilitat* OR regenerat* OR establish*) AND (China).
From these studies, we collected information about the effects of
restoration measures on plant and soil variables in degraded grasslands in
China. We selected peer-reviewed literatures based on five criteria. (1)
The studies included field measurements, thus we excluded studies derived
from laboratory incubation, remote sensing research, and model simulation.
(2) The study sites were in degraded grasslands in China. (3) Details
about the restoration regime (duration and methods) were explicitly
mentioned in the literature. (4) The studies included details about sample
size and mean of target variables from restored and degraded grasslands.
(5) The studies included at least one of the following variables (which
formed part of this meta-analysis); namely changes in plant community
biomass, structure, diversity, and physical, chemical and biological
properties of soil in the grasslands after restoration. We found 208
relevant publications about studies conducted in 103 degraded grassland
sites. We collected 21 variables related to plant community and soil
properties. These plant variables included aboveground biomass (AGB),
litter biomass (LitterB), belowground biomass (BGB), the ratio of BGB to
AGB (BAratio), plant height, plant coverage, plant density, plant species
richness, and the Shannon-Wiener diversity index (Shannon). Soil variables
included soil bulk density (BD), soil water content (SWC), pH, soil
organic C content (SOC), soil total nitrogen content (TN), soil total
phosphorus content (TP), soil available nitrogen content (AN), soil
available phosphorus content (AP), soil organic C storage (SOCS), soil
total nitrogen storage (NS), soil microbial biomass C content (MBC), and
soil microbial biomass nitrogen (MBN). The WebPlotDigitizer 4.1
(https://automeris.io/WebPlotDigitizer/) was used to digitize and extract
raw data from the studies. We recorded the following information about the
study sites: detailed name, latitude, longitude, mean annual temperature
(MAT), mean annual precipitation (MAP), grassland type, restoration
measures employed, duration of the restoration, and soil depths of BGB and
soil variables. We extracted climatic characteristics from the global
database at http://www.worldclim.org/ if MAT and/or MAP data were missing
in the literature, based on latitude and longitude coordinates.
Data compilation from observational studies in natural grasslands We
searched the ISI Web of Science and the CNKI institution for published
literatures about natural grasslands before 12 November 2020, using the
site name and grassland type provided. We aimed to evaluate the degree of
recovery of degraded grassland by comparing with natural (undisturbed)
grasslands in the same study site. In this meta-analysis, natural
grasslands were classified into three groups, which included undisturbed
and intact grasslands, reference grasslands for degradation research, and
reference grasslands as a control group for global change experiments
(i.e. N deposition, global warming, precipitation changes). Three criteria
were used to select studies about natural grasslands from the literatures.
(1) Studies had been conducted in the field. (2) Studies included
observations of the natural state of the grassland. (3) The degraded
grassland type was consistent with the natural grasslands type. We
extracted data related to the 21 variables described above. We also
collected information about grassland type, sampling area, soil depth, and
soil variables, aiming to match observations of restoration studies within
the same site. Given the spatiotemporal variability of natural grasslands,
we only included variables in our study if there were at least five
observations within one site to allow for the calculation of mean values.
Using these filters, we selected 157 publications related to natural
grasslands for further analysis.   Data analysis We used a
natural log-transformed response ratio (RR) to assess the effects of
grassland restoration and calculated the corresponding variance based the
sample size. The RR was calculated as follows (Equation 1):
RR = lnXtXc                                                                  (1) The variance of the log RR was calculated as follows (Equation 2):                                            varRR = Nt+NcNt Nc                                                                  (2) where X and N represent the mean and the sample size of observations, respectively. ‘t’ and ‘c’ represent the treatment and control group, respectively. When the control group referred to degraded grasslands, RR was expressed as ln (Res/Deg), for the RR between restored and degraded grasslands. When the control group referred to natural grasslands, the RR was expressed as ln (Res/Nat), for the RR between restored and natural grasslands. The weighted mean of the RR (RR++) for each variable was analysed using the “rma.mv” function (Viechtbauer, 2010). Since many studies have more than one RR, we treated the “study” as a random factor. The effects of grassland restoration were considered significant if the 95% confidence intervals of RR++ did not overlap with zero. Percent changes of variables were calculated as follows (Equation 3):                                              (eRR++-1)×100%                                                                  (3) Restoration studies included in the meta-analysis had different restoration approaches related to grassland type, soil depth, climatic conditions, and restoration duration. We treated restoration type, grassland type, and soil depth as categorical moderators to evaluate their effects on the responses of variables to grassland restoration. A QM test was conducted to estimate the significance of differences in the RRs between different levels of categorical moderators. Specifically, we classified restoration type into two groups: passive restoration and active restoration. In this meta-analysis, passive restoration was regarded as the spontaneous recovery occurring in degraded grasslands or abandoned old fields once disturbance ceased. Active restoration indicated human-assisted restoration measures, such as fertilization, irrigation, seeding, and shallow ploughing of degraded grasslands. Grasslands were classified into five groups: alpine meadows, alpine steppe, temperate meadows, temperate steppe, and desert grasslands. Few observations were reported in unclassified grasslands, and this category was thus omitted in the further analysis of grassland type. Soil depth was divided into categories of 0-10 cm, 10-20 cm, 20-40 cm, and >40 cm. Furthermore, meta-regressions weighted by the inverse of variance were conducted to determine the dependence of the RRs of variables on climatic conditions and duration of restoration processes. Bivariate relationships among RRs of different variables were determined using the Pearson correlation analyses. All the statistical analyses were conducted in R (R Development Core Team, 2020).
This dataset contains basic study information from 365 studies for 103
grassland sites, with 21 variables related to plant community and soil
properties.
Keywords: FOS: Biological sciences