• Key message: Natural regeneration ofP. abies(L.) H. Karst. may reach high densities in lower mountain elevations. The highest densities were found in sites with moderate light availability, with low pH, and not near the riverbank. However, age-height classes differed in the predicted magnitude of response, but were consistent in response directions. Mosses and understory species typical of coniferous forests were positively correlated with regeneration density.•Context: Norway sprucePicea abies(L.) H. Karst. in Central Europe is at risk under climate change scenarios, particularly in mountain regions. Little is known about the impact of environmental factors on the natural regeneration ofP. abiesin low-elevation mountain forests.•Aims: We aimed to assess impacts of distance from the riverbank, soil pH, and light availability on naturalP. abiesregeneration. We hypothesized that (1) naturalP. abiesregeneration would depend on light availability and soil pH and (2) there are understory plant species which may indicate the microsites suitable for natural regeneration ofP. abies.•Methods: The study was conducted in the Stołowe Mountains National Park (SW Poland, 600–800 m a.s.l.). We established 160 study plots (25 m²) for natural regeneration, light availability, soil pH, and understory vegetation assessment.•Results: The highest densities of natural regeneration ofP. abieswere observed in sites with moderate light availability (0.1–0.2 of open sky) and low pH (3.5–4.5), and located relatively far from the riverbank. Cover of 22 understory plant species were correlated with naturalP. abiesregeneration densities, mostly positively.•Conclusion: Different stages of natural regeneration ofP. abiesrevealed different regeneration niches. Most understory plant species (bryophytes and herbs typical of coniferous forests) do not compete with natural regeneration ofP. abies.

During the nineteenth and twentieth centuries, coniferous monocultures were introduced, replacing natural broadleaved forests in Central Europe, mainly for economic benefits. In the mountains, Norway spruce [Picea abies (L.) H. Karst] was introduced in large areas previously covered with beech forests and also in natural riverside habitat corridors such as river valleys, despite its negative impact on the soil environment by e.g. organic matter accumulation, decrease of soil pH and changes in C/N ratio. We aimed to check how long-term Norway spruce plantations affect species richness and diversity of soil mites along a mountain river in former mixed and broadleaved forests. The study, based on 342 samples, was carried out in Stołowe Mountains National Park (SW Poland). Understory species biomass, soil pH and soil organic layer thickness significantly affected soil mite communities. Although coniferous forests did not differ from either broadleaved or mixed forests in mite density (number of individuals m⁻²) and species diversity (H′), they were characterized by low species richness and proportional abundance of Uropodina mites typical for broadleaved forests. In total, 4849 mites classified into 57 species were recorded from all forest types and no unique species were found in the sampled forests. Although the mite communities were dominated by the same common species (Veigaia nemorensis, Paragamasus runcatellus, Leptogamasus obesus and Trachytes aegrota), they still maintain the rare species of broadleaved forests and their high recovery potential may be used in forest conversion.

Orthotrichum pulchellum is a species of epiphytic moss in which a significant expansion from the oceanic part of Europe to the east of the continent has been observed in the recent two decades. The improvement in air quality in Central and Eastern Europe, but also climate change, probably plays a role in this. This study shows what direction of its spreading we can expect in the future. Ecological niche modeling (ENM) is a widespread method to find out species niches in environmental and geographical space, which allows us to highlight areas that have a higher probability of occurrences of the studied species, based on identifying similar environmental conditions to those already known. We also made predictions for different future scenarios (CMIP5 climatology datasets for the years 2041–2060). Because we were not able to distinguish between historical and newly settled areas, and so, had to use some of the traditional approaches when modeling invasive species, we proposed to use niche clusters based on environmental layers to split the data of all known occurrences and make models separately for each cluster. This approach seems reasonable from the ecological species point of view because using all the morphologically same samples could be misleading. Altogether, 2712 samples were used from three separate niche clusters. For building the models, the Maxent algorithm was used as a well-tested, well-accepted, and commonly used method.