# Mercury Uptake and Transport by Plants in Aquatic Environments: A Meta-Analysis
## 2 Materials and Methods
### Data Processing  
Excel 2021 (Microsoft Office) was used to establish a database of the selected studies. The data entry for each study included the name of the first author, year of publication, experimental site, plant species, plant habitat, pH, sample size, mean, and standard deviation between the control and experimental groups. If the study only presented the standard error or the maximum, minimum, and median values, the standard deviation was calculated using the following formulae:  
\[\mathrm{SD}=\mathrm{SE}\times\sqrt{\mathrm{n}} \tag{1}\]  
\[\overline{X}=\frac{\mathrm{a}+2\mathrm{m}+\mathrm{b}}{4} \tag{2}\]  
\[\mathrm{SD}=\sqrt{\frac{4\overline{X^{2}+\left(\mathrm{b}-\mathrm{a}\right)^{ 2}}}{12}} \tag{3}\]  
where n represents the sample size; a and b are the maximum and minimum values, respectively; m is the median; \(\overline{X}\) is the mean; and SD and SE are the standard deviation and standard error, respectively [51]. All data provided in a chart format were extracted using Gett data Graph Digitizer 2.24 ([EMAIL_ADDRESS]).  
The data used in this study were obtained from independent experiments. For each study, the data were categorized into a control group and an experimental group. Effect size (ES) was determined based on the response ratio (R), and for each group of observations, the corresponding R and its variance (V) were calculated. The formulae used for these calculations were as follows:  
\[\text{ES}=\text{ln}(\text{R})=\text{ln}\left(\frac{\overline{\text{Xe}}}{ \overline{\text{Xe}}}\right) \tag{4}\]  
\[\text{V}=\frac{\text{Se}^{2}}{\text{ne}\overline{\text{Xe}}^{2}}+\frac{\text {Sc}^{2}}{\text{ne}\overline{\text{Xe}}^{2}} \tag{5}\]  
where \(\overline{\text{Xe}}\) and \(\overline{\text{Xe}}\) represent the means of the experimental and control groups, respectively; ne and nc represent the number of samples in the experimental and control groups, respectively; and Se and Sc represent the standard deviations of the experimental and control groups, respectively [52]. The average effect of all observations can be estimated using the following equations:  
\[\text{W}=\frac{1}{\text{V}} \tag{6}\]  
\[\overline{\text{ln}(\text{R})}\ =\frac{\sum_{i}\text{Win}(\text{Ri})}{\sum_{i} \text{Wi}} \tag{7}\]  
Figure 1: Plant species classification based on the Angiosperm Phylogeny Group III system [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50].  
where W represents the weighted factor of the influence of each observation and Ri and Wi represent the values of R and W of the ith observation, respectively [53].  
The estimation of R depended on the context of each examined study. For example, when the experimental and control groups in a particular study represented the Hg concentration in aquatic plants and those in water, respectively, R was obtained as the logarithm of the biological concentration factor (BCF), which is a measure of the ability of the plants to absorb heavy metals from the environment. When the experimental and control groups represented the Hg concentration in plant roots and those in water or sediments, respectively, R was obtained as the logarithm of the biological accumulation factor (BAF), whereas when the experimental and control groups represented the Hg concentration in plant roots and those in the aboveground plant parts, respectively, R was obtained as the logarithm of the translocation factor (TF). Therefore, we were able to derive different conclusions by interpreting the different estimations of R.