Supplementary dataset and replication materials for “Substrate affinities of slime moulds (Eumycetozoa) and their potential as indicators of forest microhabitat conditions”

Description

This supplementary package accompanies the manuscript “Substrate affinities of slime moulds (Eumycetozoa) and their potential as indicators of forest microhabitat conditions”. It contains the replication code, quality-control outputs, analysis-unit and model-frame tables, diversity summaries, substrate-effect models, indicator-species results, pH screening outputs, cross-validation and diagnostic summaries, sensitivity analyses, and figure source data required to reproduce and audit the substrate-focused analyses presented in the manuscript. All analytical result files are contained in the folder Analysis_Outputs. A detailed inventory of all files, worksheets, and their contents is provided in File_Index_and_Descriptions.pdf. The analytical workflow is based on the underlying occurrence archive: Pawłowicz T (2025). Georeferenced Checklist and Occurrence Dataset of Slime Moulds (Eumycetozoa) Across Central and Eastern Europe Emphasising Forest Ecosystems. Biodiversity Data Journal. Occurrence dataset. https://doi.org/10.15468/jqukxm These materials document data checking and harmonisation, construction of country × substrate analysis units and offsets, diversity standardisation, zero-truncated negative binomial mixed modelling, elevational affinity profiling, indicator analyses, pH screening, model validation, and sensitivity analyses.

Steps to reproduce

Download the underlying GBIF occurrence dataset cited above and place it in the same working directory as the supplementary files provided with this package. Open Replication_Code.R in R and install or load the required packages listed in the script. Run Replication_Code.R sequentially to reproduce the full workflow, including data checking and harmonisation, construction of country × substrate analysis units and offsets, diversity standardisation, GLMM fitting, elevational affinity profiling, indicator analyses, pH screening, model diagnostics, sensitivity analyses, and figure/table outputs. File-level details are provided in Index_and_Descriptions.pdf.

Institutions

• Bialystok University of Technology

  Podlasie, Bialystok

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