Wildfire And Biodiversity Meta-Analysis Dataset (European Forests)

Overview Here is the dataset, code, and results of a meta-analysis examining the effects of fires on species abundances within various forest ecological groups. The analysis focuses on how different taxa respond to fire events of varying severity, providing quantitative estimates of these responses using effect sizes. The dataset provides 2016 unique effect sizes from 1068 unique species reported in 36 studies investigating wild or prescirbed fires. The study sites covered four European forest biomes (Mediterranean Forests, woodlands and scrubs; Temperate broadleaf and mixed forests; Temperate Coniferous Forest, and Boreal forests/Taiga) and 15 ecoregions (Olson et al., 2001).  The main CSV file contains all the input and output data. The primary input data contains data from published studies examining fire effects on various taxonomic groups. Each row represents a unique effect size from a comparison between burned (treatment) and unburned (control) conditions. A second CSV file contains the citation information for the 36 included studies the main dataset. The R script contains code to (1) estimate effect sizes from the input data, and (2) run the meta-analytical model. Variable description for the main dataset Input Data Variables: Metadata study_id: Unique identifier for each study author_year: Author name(s) and publication year biome_wwf: World Wildlife Fund biome classification biome_category_wwf: Category of WWF biome ecoregion_wwf: WWF ecoregion classification country: Country where study was conducted Input Data Variables: Fire fire_type: Type of fire (e.g., prescribed, wildfire) fire_severity: Original fire severity classificationfire_severity_binned: Binned fire severity (simplified categories, years) fire_extent_km2: Extent of fire in square kilometerstime_since_fire_years: Time since fire event in years Input Data Variables: Biological group: General taxonomic group pollinator: Indicates if study examined pollinators taxa: Specific taxonomic classification (Genus or species where possible) mean_control: Mean value for control (unburned) group sd_control: Standard deviation for control group n_control: Sample size for control group mean_treatment: Mean value for treatment (burned) group sd_treatment: Standard deviation for treatment group n_treatment: Sample size for treatment group mean_control_adj: Adjusted mean for control group mean_treatment_adj: Adjusted mean for treatment group Output Data Variables: Calculated/Additional Variables ES_ID: Unique identifier for each effect size observation cv_control: Coefficient of variation for control group cv_treatment: Coefficient of variation for treatment group cv2_cont_new: Squared coefficient of variation for control group cv2_treatment_new: Squared coefficient of variation for treatment group lnrr_laj: Log response ratio (effect size) calculated using Lajeunesse method v_lnrr_1A: Variance of the log response ratio Predicted: Predicted effect size from meta-regression model Residuals: Difference between observed and predicted effect size b_CV2_1: Between-study coefficient of variation squared for control b_CV2_2: Between-study coefficient of variation squared for treatment Interpretation Notes Effect sizes (lnrr_laj) represent the natural log of the ratio between treatment (burned) and control (unburned) means Positive values indicate higher values in burned areas compared to unburned areas Negative values indicate lower values in burned areas compared to unburned areas The analysis accounts for between-study and within-study heterogeneity through the multilevel structure Software This analysis was conducted using R with the following packages:  metafor (for meta-analysis) tidyverse (for data manipulation) ggplot2 (for visualization) dmetar (for heterogeneity assessment) Analysis Methods The meta-analysis was conducted using the following approach:  1. Data Preparation: Data was cleaned and prepared for analysis. For the input data, where there were observations with a zero for either the control or the treatment (i.e., an abundance of zero), to address caveat (1), we used a commonly used adjustment factor of 0.5 (Schwarzer, 2007; Weber et al., 2020) added to the treatment and control mean if one or the other was zero. For data where the study reported both mean and standard deviation, effect sizes were calculated using log response ratios (lnrr) following Lajeunesse's method (Lajeunesse, 2015). Where SDs were not reported from each study, they were estimated these using the pooled CVs from the subset of studies that do report SDs and applied the ‘missing cases’ method (Nakagawa et al., 2023) to calculate ESs and sampling variances. Outliers were robustly identified and removed (observations with residuals > |3|). 2. Meta-Analysis Models: Multilevel mixed-effects meta-analysis was conducted using the metafor package in R. Random effects structure included nested random effects (effect sizes nested within studies). Main model included interaction between taxonomic group (group) and fire severity (fire_severity_binned). Additional models examined effects by taxonomic group alone and fire severity alone. Post-fire recovery time was also explored as a moderator (0-5 years vs. 5-10 years) 3. Model Diagnostics: Residual analysis was performed to check model assumptions. Heterogeneity was assessed using multilevel I² statistics. Predictions were generated for each combination of taxa group and fire severity. References Lajeunesse, M.J., 2015. Bias and correction for the log response ratio in ecological meta‐analysis. Ecology 96, 2056–2063. https://doi.org/10.1890/14-2402.1 Nakagawa, S., Noble, D.W.A., Lagisz, M., Spake, R., Viechtbauer, W., Senior, A.M., 2023. A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations. Ecology Letters 26, 232–244. https://doi.org/10.1111/ele.14144 Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Allnut, T.F., Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P., Kassem, K.R., 2001. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938. Schwarzer, G., 2007. meta: An R package for meta-analysis. R News 7, 40–45. Weber, F., Knapp, G., Ickstadt, K., Kundt, G., Glass, Ä., 2020. Zero‐cell corrections in random‐effects meta‐analyses. Research Synthesis Methods 11, 913–919. https://doi.org/10.1002/jrsm.1460
Publisher name: Zenodo
Last updated: 2026-02-20T14:45:08Z