https://doi.org/10.5061/dryad.c59zw3rkc

Description of the data and file structure

All empty cells will be replaced with standardized placeholders (n/a) to avoid ambiguity in Meta-analysis Data.

All variables (column names)  in Meta-analysis Data. Detailed annotation information is provided below:

Data collection and extraction

We searched peer-reviewed publications from 1990 to 2023 on the ISI Web of Science (https://www.webofscience.com/), Google Scholar (http://scholar.google.com/), China National Knowledge Infrastructure (CNKI) Open Resource (http://www.cnki.net/), and reference lists of the retrieved articles. The search terms were as follows: (plant invasive* OR alien plant OR exotic plant OR invasive plant) AND (soil organic carbon OR microbial biomass carbon OR dissolved organic carbon OR particulate organic carbon OR mineral-associated organic carbon). The same set of keywords was employed across all three databases in the search process. We reviewed each article to determine whether the studies satisfied the following criteria:

(1) The studies should provide measured data for at least one of the following seven carbon fractions: TOC, SOC, DOC, MBC, POC, MAOC, and ROC.

(2) The mean, sample size, and standard deviation (SD) or standard error (SE) of these carbon fractions should be provided directly or should be calculated from the study results. Data were obtained from the figures via Digitizer software version 4.6 (Department of Physics at the University of South Alabama, Mobile, AL, USA) .

(3) Experiments conducted under both greenhouse and field conditions were considered, and the data were analysed separately.

(4) As we focused on variations in soil carbon during plant invasion, the studies should include pairwise comparisons of at least two soil carbon categories (invasive plants vs. native plants or invasive plants vs. bare land) with significant differences (*p *< 0.05).

(5) When different publications included the same observations, we recorded the observations only once.

(6) When a study included data on different invasive species, we considered the data as distinct observations. Additionally, when a publication contained multiple experiments under different abiotic conditions, such as different sites, treatments, and soil layers, we considered each to include different observations and recorded them separately.

Finally, we obtained a meta-dataset of 445 observations covering 61 studies. For each study, we extracted various carbon fractions. In addition, we obtained data on plant families, geographical location (latitude and longitude), ecosystem type, and soil sampling depth from the original publications.

Code/software

We employed the natural log-transformed response ratio (lnRR) as the effect size for various types of soil carbon affected by plant invasion. The lnRR can be calculated as follows:

where  represents the mean content of the various types of carbon in soil in the area where invasive plants are located, and Xc represents the mean content of each type of carbon in soil in the area where native plants are located. To solve the problem of the occurrence of RR values of zero in the numerator, we replaced them with 10% of the lowest value measured within the same sample . The total effect is the average effect across all samples.

We calculated the variance (v) in the effect size of the individual observations as follows:

where St and Sc are the SDs of the experimental and control groups, respectively, and nt and nc are the sample sizes of the experimental and control groups, respectively.

The metafor package in R v. 4.0.2 (R Core Team, Vienna, Austria) was used to derive the effect size for each variable. The weighted response ratio (RR++), 95% bootstrap confidence interval (CI) and SE *S *(RR++) were calculated via a random effects model.

 

 

If the 95% bootstrap CI contained the value of 0, the invasion of exotic species did not significantly affect the variables. If 95% bootstrap CI > 0, the invasion of exotic species significantly increased the amount of soil carbon (p < 0.05). If 95% bootstrap CI < 0, the invasion of exotic species significantly decreased the amount of soil carbon (*p *<0.05) .

We employed a random effects model to conduct heterogeneity tests, thereby assessing the impact and quantifying the degree of influence of invasive plants on various types of soil carbon across different ecosystem types (forests, estuaries, etc.), as well as the impact of invasive plants on the various carbon types at different soil depths.

 To assess publication bias, we used funnel plots, which are graphical tools that help visualize the distribution of studies in a meta-analysis. Funnel plots were constructed with the effect size (lnRR) on the x-axis and the SE of the effect size on the y-axis. Funnel plot symmetry is a key indicator of the absence of publication bias. Notably, if the plot is symmetric, with studies evenly distributed around the central axis, this suggests that there is no publication bias. Conversely, asymmetry may indicate the presence of publication bias. To quantitatively evaluate publication bias, we employed Egger's regression test, in which the SE of the effect size is regressed against the effect size itself. A p value greater than 0.05 suggests no publication bias, whereas a p value less than 0.05 indicates the presence of publication bias. If publication bias was detected (p< 0.05), we applied the trim-and-fill method to reanalyse the data. This method involves mirroring any missing studies in the funnel plot to estimate potential unpublished studies and then applying a random effects model to reanalyse the imputed data, which yields the final pooled effect size and its SE. Funnel plots were constructed for the data used in each analysis. Therefore, this approach helps ensure that our conclusions are robust and not unduly influenced by publication bias.

Access information (Data Source)

Data was derived from the following sources:

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