Diversity Patterns of Macrofungi in Xerothermic Grasslands from the Nida Basin (Małopolska Upland, Southern Poland): A Case Study

Additions to the genus Mycena (Mycenaceae, Agaricales): Descriptions of five new taxa in Hunan Province, China

Few studies have been conducted on Mycena species in Hunan Province, China. During our research on the species diversity of Mycena in Hunan Province, we identified approximately 30 Mycena species based on morphological and molecular evidence. Five species are recognized herein as new to science, namely, M.fulvomarginata, M.huangsangensis, M.hongfengensis, M.subroriduliformis, and M.roseolamellata. The phylogenetic analyses of a combined ITS and LSU sequence dataset revealed that five new species each formed an independent lineage that could separate phenotypically similar and phylogenetically related species. Descriptions, photographs, and phylogenetic analysis results are provided for the five new species, along with the comparisons with related species. A key to all Mycena species found in Hunan is also provided.

Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network

The occurrence and regularity of macrofungal fruitbody formation are influenced by meteorological conditions; however, there is a scarcity of data about the use of machine-learning techniques to estimate their occurrence based on meteorological indicators. Therefore, we employed an artificial neural network (ANN) to forecast fruitbody occurrence in mycorrhizal species of Russula and Amanita, utilizing meteorological factors and validating the accuracy of the forecast of fruitbody formation. Fungal data were collected from two locations in Western Hungary between 2015 and 2020. The ANN was the commonly used algorithm for classification problems: feed-forward multilayer perceptrons with a backpropagation algorithm to estimate the binary (Yes/No) classification of fruitbody appearance in natural and undisturbed forests. The verification indices resulted in two outcomes: however, development is most often studied by genus level, we established a more successful, new model per species. Furthermore, the algorithm is able to successfully estimate fruitbody formations with medium to high accuracy (60-80%). Therefore, this work was the first to reliably utilise the ANN approach of estimating fruitbody occurrence based on meteorological parameters of mycorrhizal specified with an extended vegetation period. These findings can assist in field mycological investigations that utilize sporocarp occurrences to ascertain species abundance.