Bryophytes as bioindicators in natural pine forests on mineral soils in Poland

Although bryophytes are often underrepresented in vegetation classifications, our study demonstrates their crucial role in differentiating pine forest communities and highlights their untapped potential as ecological indicators. This is the first application of a Kohonen Artificial Neural Network (KANN) to classify pine forests using only bryophyte species abundance. Based on this input, KANN consistently distinguished four forest associations: Cladonio-Pinetum , Empetro nigri-Pinetum , Leucobryo-Pinetum – Peucedano - Pinetum complex, and Molinio-Pinetum . This clear separation, achieved solely through bryophyte abundance data, underscores their diagnostic value. For the forest types delineated by KANN, indicator species analysis identified 26 taxa as bioindicators, with 19 showing highly significant associations ( p < 0.001) with specific phytocoenoses: five for Cladonio-Pinetum : Dicranum scoparium , D . spurium , Hypnum jutlandicum , Pohlia nutans , and Ptilidium ciliare ; two for Leucobryo-Pinetum–Peucedano-Pinetum complex: Leucobryum glaucum and Pleurozium schreberi ; ten for Molinio-Pinetum , the most diverse association in terms of indicative taxa: Aulacomnium palustre , Brachythecium rutabulum , Lophocolea bidentata , Plagiothecium denticulatum , Polytrichum commune , P . formosum , Sciuro-hypnum oedipodium , Sphagnum capillifolium , S . fallax , and Tetraphis pellucida . Notably, the literature to date has associated only P . commune and Sphagnum sp. with this community; two species for Empetro nigri-Pinetum : Hylocomium splendens and Pseudoscleropodium purum , although only P . purum has previously been recognized as indicative for this phytocoenosis. The identification of a greater number of indicative bryophyte species than previously acknowledged suggests that their ecological relevance in pine forests is substantially underestimated. Given the scarcity of bryophyte-focused approaches in ecological monitoring, our findings advocate for their greater inclusion in vegetation analyses and indicator frameworks.