Bryophytes are a group of small, non-vascular plants that include mosses, liverworts, and hornworts

In 2023, field research in south-western Poland led to the noteworthy discovery of two hornworts: Notothylas orbicularis, a species previously unrecorded in this country, and the rediscovery of Anthoceros neesii for the Polish bryoflora. These findings are significant as they suggest a response to climate change, which is facilitating the range expansion of hornworts within Central Europe. Detailed descriptions of the new localities for both species are provided, highlighting the specific environmental conditions and habitats where they were found. Distribution maps for Notothylas orbicularis and Anthoceros neesii in Poland are provided, as well as SEM micrographs of spores. Additionally, a key to the identification of Polish hornwort species is also included. Furthermore, a model projecting the potential future spread of these hornworts within Poland and the broader Central European region is presented. This model considers climatic variables and habitat availability, offering insights into possible range shifts. This study contributes to the growing body of evidence that climate change is a driving factor in the redistribution of bryophytes.

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.

The study presents the distribution of the genera Orthotrichum s.l. and Nyholmiella in the phytogeographical district 98. Nízký Jeseník, and follows the project carried out between years 2005 and 2009 in nearby phytogeographical district 97. Hrubý Jeseník. The total number of 57 locations was visited during the year 2015, and 142 samples of mosses were collected. Altogether, 9 species of genus Orthotrichum s.l. and 1 species of the genus Nyholmiella were recently recorded in the studied area. Lists of species and localities are given.

Species distribution modeling (SDM) has come a long way since its inception. Starting as simple bioclimatic envelope models based on expert knowledge, species distribution models (SDMs) have evolved into complex and sophisticated models that incorporate multiple sources of data and machine learning algorithms. Today, SDMs play a crucial role in addressing pressing conservation and management issues, including the impacts of climate change on species ranges and the as-sessment of species vulnerability to extinction. In this article, we will embark on a journey through the history, present, and future of SDM, exploring its evolution from bioclimatic envelopes to machine learning. We will also provide practical tips on how to use SDMs effectively and discuss the exciting future developments in this field. Whether you are a seasoned SDM expert or new to this field, this article will provide valuable insights into the exciting world of SDM. By exploring the rich history and current state of the field, we hope to shed light on the tremendous potential of SDM for improving our understanding of the distribution of species in a changing world.

This study applies MaxEnt modeling to predict the future distribution of the invasive plant Heracleum mantegazzianum in Central Europe (mainly the Czech Republic, Slovakia, Austria, Germany, and Poland) for two time periods, 2011–2040 and 2041–2070. A baseline model was also developed for the historical period 1980–2010 to serve as a reference for comparison with future projections. High-resolution CHELSA bioclimatic variables were combined with occurrence records from the Species Occurrence Database (NDOP) of the Nature Conservation Agency of the Czech Republic (AOPK ČR) and from the Global Biodiversity Information Facility (GBIF), an international open access database of biodiversity records, to create species distribution models. The models achieved fair to good predictive performance and revealed clear spatial shifts between the historical baseline (1980–2010) and future projections for 2011–2040 and 2041–2070. Areas with high habitat suitability are expected to expand into regions that were only moderately suitable in the past, with southern and western Germany showing the most pronounced increases. Differences between low emission (SSP126) and high emission (SSP585) climate scenarios were limited, suggesting that the species occupies a relatively stable climatic niche. Environmental novelty analyses identified regions of increasing uncertainty due to emerging climatic conditions, highlighting the need for cautious interpretation of projections for 2041–2070. These results highlight priority areas for monitoring and management and demonstrate how climate change may drive the future spread of this invasive species.