ON STRONG TACTICAL DECOMPOSITIONS

If a 2-(v, k, k) design 2) admits a tactical decomposition with d point classes and c block classes then b — v ^ c—d ^ 0, (see [3]). Decompositions for which b+d = v + c are of special interest (see for instance [1]), and are called strong. Any tactical decomposition of a symmetric design is strong. A strong tactical decomposition of

Hebeloma hiemale Bres. in Arctic/Alpine Habitats

Hebeloma hiemale Bres. is a frequently collected species and occurs with many hosts in a wide variety of habitats. It is an ectomycorrhizal species that has been recorded with a broad range of both deciduous and coniferous trees, including Salix and Populus. However, it is not generally regarded as an arctic/alpine fungus and we are not aware of any previous reports of H. hiemale in these habitats. During ISAM VIII 2008 this species was collected with Salix reticulata on the Beartooth Plateau in Montana above tree-line at 3000 m asl. We also report a number of other arctic or alpine collections of H. hiemale from Greenland, Svalbard, Iceland and the U.S.A. (Wyoming and Colorado). This is the first report of H. hiemale from the Rocky Mountain alpine zone

Decrypting the Hebeloma crustuliniforme complex : European species of Hebeloma section Denudata subsect. Denudata (Agaricales)

Hebeloma subsection Denudata includes the type of H. section Denudata, Hebeloma crustuliniforme, as well as the majority of the taxa commonly included in the Hebeloma crustuliniforme complex. Complementing the work of D.K. Aanen and co-workers, and using refined morphological and molecular methods we were able to recognize further individual taxa within the section. Fifteen species occurring in Europe are assigned to H. subsect. Denudata. Of these, we describe eight species as new, namely H. aanenii, H. aurantioumbrinum, H. geminatum, H. louiseae, H. luteicystidiatum, H. pallidolabiatum, H. perexiguum and H. salicicola. Naucoria bellotiana, a species very similar to H. alpinum is recombined into Hebeloma. A key to Hebeloma subsect. Denudata is provided. We demonstrate that within this subsection there is good overall consistency between morphological, phylogenetic and biological species concepts. In contrast to current opinion, in this group there is little species overlap, particularly when also considering species frequencies, between arctic and alpine floras on one hand and temperate on the other

Epitypification of Hebeloma crustuliniforme

It is widely recognised that there is no consistent use of the name H. crustuliniforme. The name has been used to represent a number of different taxa and indeed taxa from sections of Hebeloma that are morphologically and molecularly well separated. The holotype of H. crustuliniforme is plate 308 Bulliard from 1787 and while it can be interpreted, no such interpretation can be without ambiguity. It is clear from existing literature that modern authors have applied numerous different interpretations to this name and no real consensus exists. Indeed it appears that at various times most of the medium to large species within both sections Denudata and Velutipes Vesterh. have been referred to as H. crustuliniforme. Within this paper an epitype is selected for H. crustuliniforme in order to give the taxon a precise meaning and a detailed species description is given. Molecular data combined with the results of intercompatibility tests of Aanen and Kuyper published earlier support the definition of H. crustuliniforme adopted in this paper as a distinct taxon and as a biological species. We strongly recommend that this taxon be referred to as Hebeloma crustuliniforme (Bull.) Qu,l. emend. Vesterh., U. Eberh. & Beker in order to emphasise that it is the specific taxon rather than the complex to which it is being referred

Hebelomina (Agaricales) revisited and abandoned

Background and aims – The genus Hebelomina was established in 1935 by Maire to accommodate the new species Hebelomina domardiana, a white-spored mushroom resembling a pale Hebeloma in all aspects other than its spores. Since that time a further five species have been ascribed to the genus and one similar species within the genus Hebeloma. In total, we have studied seventeen collections that have been assigned to these seven species of Hebelomina. We provide a synopsis of the available knowledge on Hebelomina species and Hebelomina-like collections and their taxonomic placement.Methods – Hebelomina-like collections and type collections of Hebelomina species were examined morphologically and molecularly. Ribosomal RNA sequence data were used to clarify the taxonomic placement of species and collections.Key results – Hebelomina is shown to be polyphyletic and members belong to four different genera (Gymnopilus, Hebeloma, Tubaria and incertae sedis), all members of different families and clades. All but one of the species are pigment-deviant forms of normally brown-spored taxa. The type of the genus had been transferred to Hebeloma, and Vesterholt and co-workers proposed that Hebelomina be given status as a subsection of Hebeloma. In the meantime, Hebelomina-like Hebeloma, belonging to seven different species in three different sections, have been found. We conclude that Hebelomina should be abandoned as a supraspecific taxon

Machine Learning for Species Identification: The HebelomaProject from database to website

Attempts to use machine learning (ML) for species identification of macrofungi have usually involved the use of image recognition to deduce the species from photographs, sometimes combining this with collection metadata. Our approach is different: we use a set of quantified morphological characters (for example, the average length of the spores) and locality (GPS coordinates). Using this data alone, the machine can learn to differentiate between species.Our case study is the genus Hebeloma, fungi within the order Agaricales, where species determination is renowned as a difficult problem. Whether it is as a result of recent speciation, the plasticity of the species, hybridization or stasis is a difficult question to answer. What is sure is that this has led to difficulties with species delimitation and consequently a controversial taxonomy.The Hebeloma Project—our attempt to solve this problem by rigorously understanding the genus—has been evolving for over 20 years. We began organizing collections in a database in 2003. The database now has over 10,000 collections, from around the world, with not only metadata but also morphological descriptions and photographs, both macroscopic and microscopic, as well as molecular data including at least an internal transcribed spacer (ITS) sequence (generally, but not universally, accepted as a DNA barcode marker for fungi (Schoch et al. 2012)), and in many cases sequences of several loci. Included within this set of collections are almost all type specimens worldwide. The collections on the database have been analysed and compared. The analysis uses both the morphological and molecular data as well as information about habitat and location. In this way, almost all collections are assigned to a species. This development has been enabled and assisted by citizen scientists from around the globe, collecting and recording information about their finds as well as preserving material.From this database, we have built a website, which updates as the database updates. The website (hebeloma.org) is currently undergoing beta testing prior to a public launch. It includes up-to-date species descriptions, which are generated by amalgamating the data from the collections of each species in the database. Additional tools allow the user to explore those species with similar habitat preferences, or those from a particular biogeographic area. The user is also able to compare a range of characters of different species via an interactive plotter.The ML-based species identifier is featured on the website. The standardised storage of the collection data on the database forms the backbone for the identifier. A portion of the collections on the database are (almost) randomly selected as a training set for the learning phase of the algorithm. The learning is “supervised” in the sense that collections in the training set have been pre-assigned to a species by expert analysis. With the learning phase complete, the remainder of the database collections may then be used for testing. To use the species identifier on the website, a user inputs the same small number of morphological characters used to train the tool and it promptly returns the most likely species represented, ranked in order of probability.As well as describing the neural network behind the species identifier tool, we will demonstrate it in action on the website, present the successful results it has had in testing to date and discuss its current limitations and possible generalizations

Machine Learning for Species Identification: The HebelomaProject from database to website

Attempts to use machine learning (ML) for species identification of macrofungi have usually involved the use of image recognition to deduce the species from photographs, sometimes combining this with collection metadata. Our approach is different: we use a set of quantified morphological characters (for example, the average length of the spores) and locality (GPS coordinates). Using this data alone, the machine can learn to differentiate between species.Our case study is the genus Hebeloma, fungi within the order Agaricales, where species determination is renowned as a difficult problem. Whether it is as a result of recent speciation, the plasticity of the species, hybridization or stasis is a difficult question to answer. What is sure is that this has led to difficulties with species delimitation and consequently a controversial taxonomy.The Hebeloma Project—our attempt to solve this problem by rigorously understanding the genus—has been evolving for over 20 years. We began organizing collections in a database in 2003. The database now has over 10,000 collections, from around the world, with not only metadata but also morphological descriptions and photographs, both macroscopic and microscopic, as well as molecular data including at least an internal transcribed spacer (ITS) sequence (generally, but not universally, accepted as a DNA barcode marker for fungi (Schoch et al. 2012)), and in many cases sequences of several loci. Included within this set of collections are almost all type specimens worldwide. The collections on the database have been analysed and compared. The analysis uses both the morphological and molecular data as well as information about habitat and location. In this way, almost all collections are assigned to a species. This development has been enabled and assisted by citizen scientists from around the globe, collecting and recording information about their finds as well as preserving material.From this database, we have built a website, which updates as the database updates. The website (hebeloma.org) is currently undergoing beta testing prior to a public launch. It includes up-to-date species descriptions, which are generated by amalgamating the data from the collections of each species in the database. Additional tools allow the user to explore those species with similar habitat preferences, or those from a particular biogeographic area. The user is also able to compare a range of characters of different species via an interactive plotter.The ML-based species identifier is featured on the website. The standardised storage of the collection data on the database forms the backbone for the identifier. A portion of the collections on the database are (almost) randomly selected as a training set for the learning phase of the algorithm. The learning is “supervised” in the sense that collections in the training set have been pre-assigned to a species by expert analysis. With the learning phase complete, the remainder of the database collections may then be used for testing. To use the species identifier on the website, a user inputs the same small number of morphological characters used to train the tool and it promptly returns the most likely species represented, ranked in order of probability.As well as describing the neural network behind the species identifier tool, we will demonstrate it in action on the website, present the successful results it has had in testing to date and discuss its current limitations and possible generalizations