The name OLICH refers to “contributions from the Oslo lichen herbarium”.

Gunnhild Marthinsen (project coordinator), Siri Rui (project engineer), Einar Timdal (taxonomic supervisor), all affiliated to the Natural History Museum, University of Oslo.

The Nordic countries, including Svalbard: Norway, mainland (97.0% of the specimens), Sweden (2.0%), Finland (0.5%), Norway, Svalbard (0.5%), Fig. 1.

Figure 1.  

Distribution of the OLICH records

This project is funded by the Norwegian Research Council (RCN 226134) through the Norwegian Barcode of Life project (NorBOL). In addition, the Norwegian Species Information Centre (Artsdatabanken) has funded four projects on lichens under the Norwegian Taxonomy Initiative from which material has been collected and/or sampled (project numbers 70184216, 70184227, 70184233, and 70184237).

The material was partly freshly collected specimens from the field and partly previously curated specimens in university herbaria. The distribution of specimen age at seqencing is given in Fig. 2. The specimens were initially determined by experts based on morphology and secondary chemistry and the identifications were re-examined at the quality control after sequence acquisition. All collected material were, after sampling, deposited in university herbaria and the entire material is housed in BG (2.1%), H (0.1%), O (94.9%), SAV (0.2%), TRH (1.7%) and TROM (1.1%) (abbreviated according to Index Herbariorum; http://sweetgum.nybg.org/science/ih).

Figure 2.  

Distribution of specimen age (years after collecting) at sequencing in the OLICH dataset.

Thin-layer chromatography (TLC) was performed for identification of secondary compounds on selected specimens in accordance with the methods of Culberson (1972), as modified by Menlove (1974) and Culberson and Johnson (1982). Solvent system B' was used for routine examination, A and C for additional confirmation.

Tissue of thallus or preferably apothecia (about 3 mm³) was assembled in 96 well plates obtained from the Canadian Centre for DNA Barcoding (CCDB; http://ccdb.ca). One well was left empty to serve as negative control. The freshly collected material was either sampled in the field or at a field station. In the field, the specimen was first photographed, then tissue was removed with a flame-decontaminated forceps and placed in the well and finally the specimen was collected. Specimens requiring more controlled handling were photographed and sampled either with forceps or scalpel under a binocular lens at a field station. Wells containing fresh material were dried for several days with lids open in a drying chamber containing silica gel. Herbarium material was sampled at the housing institutions under binocular lenses.

Field data was stored on tablets and GPSs. Tissue sampling data and photography data were transferred directly to standard BOLD spreadsheets, whereas the collection data passed through herbarium databases for obtaining institution storing, voucher ID (= sample ID), uniform nomenclature and taxonomical hierarchy and contact information. Material sampled in herbaria used a similar pipeline, although simpler, as collection data was already available.

The plates were sent to CCDB for DNA extraction (following Ivanova et al. 2011) and sequencing of the ITS region (following Kuzmina and Ivanova 2011). Primers used were ITS1-F or ITS5 (forward) and ITS4 (reverse) (Gardes and Bruns 1993, White et al. 1990). Trace files and sequences were uploaded to BOLD (Ratnasingham and Hebert 2007). The sequences were edited at CCDB, but occasionally inspected and re-edited at OLICH.

All OLICH sequences were subjected to a local BLASTn search against a database consisting of all lichen ITS sequences in GenBank (downloaded 26-06-2018) and all unpublished lichen ITS sequences were produced at various projects at our own institution, including the OLICH dataset. We inspected automatically generated distance-based neighbour-joining trees for all genera represented in the OLICH dataset, i.e. we merged all OLICH sequences with all GenBank ITS sequences from the same genus before running ClustalW2 (http://www.clustal.org/clustal2) in batch mode (one run for each genus) and saving the trees. For selected cases (Figs. 5–15), phylogenetic analyses were performed through the following pipeline: selecting relevant additional sequences for comparison from the GenBank download; aligning the dataset using the online version of MAFFT (https://mafft.cbrc.jp/alignment/server); trimming ends and manually editing the alignment using BioEdit 7.1.3.0 (Hall 1999); analysing the alignment by the maximum likelihood method using RAxML v. 8.2.10 (Stamatakis 2014) by applying the “ML + rapid bootstrap” method with 100 bootstrap repeats and the GTRGAMMA substitution model; and editing the best-scoring tree using TreeGraph2 (Stöver and Müller 2010).

To further check that the sequences and species determinations were correct and also to check whether species delimitation, based on traditional characters, coincides with ITS species delimitation, we performed barcode gap analyses. The three genera in our dataset with most sampled species were chosen for these analyses; Cladonia, Ramalina and Umbilicaria. Sequences were aligned separately for each genus using the ClustalW algorithm implemented in MEGA7 (Kumar et al. 2016), with additional manual editing. Pairwise genetic distances between all specimens within each of the three genera were calculated in MEGA7, using the K2P substitution model. The distances were summarised in a bar plot, based on a histogram of all pairwise distances. For each species pair within the genera, we plotted the maximum intraspecific distance against the minimum distance to nearest neighbour-species.

The Nordic countries, including Svalbard (Fig. 1).

58.02°N and 78.94°N Latitude; 5.06°E and 31.08°E Longitude.

The OLICH dataset comprises 1324 records of 507 species belonging to 25 orders, 8 classes and 2 phyla of fungi (Table 1). The Nordic lichen flora comprises some 2400–2500 lichenised species (2387 are listed in Santesson et al. (2004)), i.e. the dataset comprises about 20–21% of the currently known Nordic species. Several taxonomic groups are still poorly understood, however, making the production of authoritative DNA-barcodes for many species premature due to uncertain taxonomy and/or lack of fresh material. The dataset is hence biased towards the more well-known groups, mainly the macrolichens in the orders Lecanorales (233 spp.), Peltigerales (62 spp.) and Caliciales (37 spp.) of the class Lecanoromycetes (Ascomycota). The taxonomy and nomenclature follows Nordin et al. (2019) unless commented on under Observations below.