DOI: 10.5281/zenodo.10251772
title: Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis

description: The zip-file contains the data and code accompanying the paper 'Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis'. Together, these files should allow for the replication of the results.
The 'raw_data' folder contains the 'MA_database.csv' file, which contains the extracted data from all primary studies that are used in the analysis. Furthermore, this folder contains the file 'MA_database_description.txt', which gives a description of each data column in the database.
The 'derived_data' folder contains the files that are produced by the R-scripts in this study and used for data analysis. The 'MA_database_processed.csv' and 'MA_database_processed.RData' files contain the converted raw database that is suitable for analysis. The 'DB_IA_subsets.RData' file contains the 'Individual Abundance' (IA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria. The 'DB_IA_VCV_matrices.RData' contains for all IA data subsets the variance-covariance (VCV) matrices. The 'DB_AM_subsets.RData' file contains the 'Total Abundance' (TA) and 'Mean Abundance' (MA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria.
The 'output_data' folder contains maps with the output data for each data subset (i.e. for each metric, taxonomic group and set of inclusion criteria). For each data subset, the map contains random effects selection results ('Results1_REsel_<subset>.csv'), the fixed effects selection results ('Results2_FEsel_<subset>.csv'), the random variance components and R^2 values for the best models subset ('Results3_BestModels_<subset>.csv'), the parameter value estimations for the fixed effects ('Results4_Parameters_<subset>.csv'), the standard errors for the estimated parameter values ('Results5_SE_<subset>.csv'), and the consensus model parameter values ('Results6_ConsensusModel_<subset>.csv'). Furthermore, each map contains a file with the best-selected random effects model structure ('BestRanEf_<subset>.RData') and a file with the consensus model ('ConsensusModel_<subset>.RData').
The 'scripts' folder contains all R-scripts that we used for this study. The 'PrepareData.R' script takes the database as input and adjusts the file so that it can be used for data analysis. The 'PrepareDataIA.R' and 'PrepareDataAM.R' scripts make subsets of the data and prepare the data for the meta-regression analysis and mixed-effects regression analysis, respectively. The regression analyses are performed in the 'SelectModelsIA.R' and 'SelectModelsAM.R' scripts to calculate the regression model results for the IA metric and MA/TA metrics, respectively. These scripts require the 'RandomAndFixedEffects.R' script, containing the random and fixed effects parameter combinations, as well as the 'Functions.R' script. The 'CreateMap.R' script creates a global map with the location of all studies included in the analysis (figure 1 in the paper). The 'CreateForestPlots.R' script creates plots showing the IA data distribution for both taxonomic groups (figure 2 in the paper). The 'CreateHeatMaps.R' script creates heat maps for all metrics and taxonomic groups (figure 3 in the paper, figures S11.1 and S11.2 in the appendix). The 'CalculateStatistics.R' script calculates the descriptive statistics that are reported throughout the paper, and creates the figures that describe the dataset characteristics (figures S3.1 to S3.5 in the appendix). The 'CreateFunnelPlots.R' script creates the funnel plots for both taxonomic groups (figures S6.1 and S6.2 in the appendix) and performs Egger's tests. The 'CreateControlGraphs.R' script creates graphs showing the dependency of the nutrient response to control concentrations for all metrics and taxonomic groups (figures S10.1 and S10.2 in the appendix).
The 'figures' folder contains all figures that are included in this study.