Wildfire and Biodiversity Meta-Analysis Dataset (European Forests)

Overview Here is the dataset of a meta-analysis examining the effects of fires on taxa abundances within various European forest fauna and flora. The analysis focuses on how different taxa respond to fire, providing quantitative estimates of these responses using effect sizes. The dataset provides 2192 unique effect sizes from 819 unique taxa reported in 29 studies investigating wild or prescribed 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 13 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 29 included studies the main dataset. Variable description for the main dataset Input Data Variables: Metadata study_id: Unique identifier for each study ES_ID: Unique identifier for each effect size observation doi: Digital object identifier for the study author_year: Author name(s) and publication year publication_year: Publication year study_latitude: The study latitude in decimal degreess study_longitude: The study longitude in decimal degrees coordinate_method: Were coordinates extracted form the study, or estimated/inferred country: Country were the study was conducted continent: 'Europe' for all biome_wwf: World Wildlife Fund biome classification biome_category_wwf: Category of WWF biome ecoregion_wwf: WWF ecoregion classification Input Data Variables: Fire fire_type: Type of fire (e.g., prescribed P, wildfire W) burned_times: The number of times the patch burned (if available) time_since_fire_years: post-fire assessment time time_since_fire: binned post-fire assessment time (<1 year, 1-5 years, 5-10 years, 10+ years) fire_extent: extent of fire (unitless) fire_extent_unit: extent of fire unit (to be read with fire_extent) fire_extent_km2: fire_extent converted to square kilometers fire_severity: Fire severity classification (L = Low, M = Moderate, H = High) control: description of the control area treatment: description of the treatment area (single_burn; multiple_burns) Input Data Variables: Biological taxa: the original taxa name from each study taxonomic_group: aggregate taxa into general groups, used for analysis kingdom: Taxonomic ranking phylum: Taxonomic ranking class: Taxonomic ranking subclass: Taxonomic ranking order: Taxonomic ranking suborder: Taxonomic ranking infraorder: Taxonomic ranking superfamily: Taxonomic ranking family: Taxonomic ranking subfamily: Taxonomic ranking genus_species: Taxonomic ranking metric: the type of metric measured inthe study to infer abundance metric_unit: the unit of the metric (above) metric_type: 'abundance" for all central_tendency_type: mean, median se_type: if variation was reported as SE, SD, or not at all mean_control: Mean value for control (unburned) se_control: SE value for the control (unburned)  sd_control: Standard deviation for control (unburned). Either extracted directly from a study, or calculated from se_control. n_control: Sample size for control (unburned) mean_treatment: Mean value for treatment (burned) se_treatment: SE value for the treatment (burned)  sd_treatment: Standard deviation for treatment (burned). Either extracted directly from a study, or calculated from se_treatment. n_treatment: Sample size for treatment (burned) mean_control_adj: Adjusted mean for control (unburned) mean_treatment_adj: Adjusted mean for treatment (burned) Output Data Variables: Calculated/Additional Variables 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 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 0.001 (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).  3. Model Diagnostics: Residual analysis was performed to check model assumptions. Heterogeneity was assessed using multilevel I² statistics.  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