Forest Habitats, Management Intensity, and Elevation as Drivers of Eumycetozoa Distributions and Their Utility as Bioindicators

Slime moulds (Eumycetozoa) are closely associated with forest structure, moisture and the availability of microhabitats, which together make them promising candidates for bioindication. This study synthesised an integrated, georeferenced resource from Central and Eastern Europe to assess how forest habitat, management intensity, and elevation structure assemblages, and to identify indicator taxa suited to monitoring. Analyses in R (RStudio, version 4.5.2) combined effort-controlled diversity comparisons, models of record intensity, habitat-stratified elevation responses, constrained ordination, and indicator testing at species and higher ranks. The resulting corpus encompassed 624 species from 16 countries and eight consolidated forest habitat classes, enabling quantification of joint assemblage responses to habitat, management intensity, and elevation under effort-controlled sampling, and facilitating the identification of indicator sets that are robust to uneven sampling. At the order and genus levels, Physarales, Trichiales, and Stemonitidales, together with genera such as Trichia, Meriderma, and Polyschismium, exhibited the clearest and most transferable indicator behaviour, while species including Trichia varia, Fuligo septica, and Meriderma carestiae emerged as promising candidates for fine-grained bioindication along habitat and elevation gradients. Habitat exerted clearer contrasts than management; elevation effects were strongly habitat specific, and a compact set of taxa showed stable, interpretable indicator behaviour across gradients. These indicator assemblages, together with an appraisal of cross-country generalisation, provide an operational basis for elevation-aware, habitat-structured bioindication with slime moulds in European forests. Taken together, these results indicate that slime mould assemblages have the potential to complement existing forest bioindication systems, both by tracking broad forest habitat types along management and elevation gradients and by providing indirect information on less conspicuous attributes such as stand naturalness and the availability of dead wood, although such applications remain at a proof-of-concept stage and will require further targeted evaluation before operational deployment.