Data for: The Biotic Floor of Old-Growth Forests: Rove Beetles (Staphylinidae) as Sentinels of Soil–Litter Tipping Points After Wildfire
Description
This dataset contains the raw ecological and environmental data collected one year post-fire (2022) in the Acatti old-growth forest (Aspromonte National Park, Italy). The study investigates the impact of wildfire severity, quantified via the differenced Normalized Burn Ratio (dNBR), on the assemblages of Rove Beetles (Coleoptera: Staphylinidae). The file Staphylinidae_Data.xlsx is organized into the following components: • Taxonomic Data: Abundance and richness counts for 1,741 specimens across 34 species, identified from 30 pitfall traps distributed along a fire severity gradient (Burned, Transition, and Unburned sites). • Environmental Covariates: Site-specific radiometric fire severity values (dNBR) derived from Sentinel-2 imagery. • Soil Biochemical Indicators: Measurements of Soil Organic Matter (SOM), Dehydrogenase activity (DHA), and Catalase activity (CAT) for each sampling unit. • Ecological Traits: Information on species-specific traits, including dispersal ability (fliers vs. flightless) and habitat specialization (old-growth specialists vs. generalists) used for IndVal and multivariate analyses. These data support the "biotic floor" hypothesis, identifying a critical ecological threshold at dNBR ≈ 0.25–0.50 where soil biodiversity suffers functional homogenization. Keywords biotic floor; Staphylinidae; dNBR; old-growth forest; wildfire severity; soil bioindicators R Script for ReproducibilityThe file Analysis_Scripts.pdf provides the fully annotated R code used to perform the statistical analyses and generate the figures presented in the manuscript. It is structured into the following sections: Environment Setup: Loading of required libraries including glmmTMB, vegan, and indicspecies. Data Pre-processing: Steps for month assignment, species filtering (excluding taxa <1% for multivariate tests), and matrix preparation. Statistical Modeling: Implementation of the GLMM (Negative Binomial) to model the abundance decay ($\beta = -0.971$) in relation to dNBR. Community Analysis: Code for NMDS ordination and PERMANOVA to test the impact of fire severity and soil biochemical drivers (Catalase and DHA) on community divergence. Bioindicator Identification: Application of the IndVal method to identify sensitive specialists and resilient taxa.
Files
Steps to reproduce
Steps to Reproduce To replicate the analyses and figures presented in the study, please follow these steps: Software Requirements: Ensure you have R (version 4.0 or higher) installed. You will also need the following packages: readxl, glmmTMB, vegan, ggplot2, indicspecies, ggeffects, and MASS. File Preparation: Download both Staphylinidae_Data.xlsx and Analysis_Scripts.pdf into the same local working directory. Data Loading: Open your R environment and set the working directory to the folder containing the files. The script uses the command read_excel("Staphylinidae_Data.xlsx") to import the raw data. Workflow Execution: Data Preparation: Run this section to assign sampling months and define the site color palettes (Fire colors: A=Red, B=Orange, C=Green). GLMM Modeling: Execute this section to reproduce the abundance decay model ($\beta = -0.971, p=0.012$) and the threshold visualization. Community Analysis: Run the code for NMDS ordination ($stress=0.08$) and PERMANOVA to test the impact of fire severity ($R^2=0.073$) and soil biochemical drivers. Graphic Output: The script is configured to generate and save Figures 1-4 in .tiff format at 300 DP
Institutions
- University of Reggio CalabriaCalabria, Reggio Calabria