Mycena species can be opportunist-generalist plant root invaders

ACKNOWLEDGEMENTS We thank Karl-Henrik Larsson and Arne Aronsen for provisions of specimens from the Natural History Museum of Oslo and help with the identification of field specimens from Svalbard. We further thank Cecilie Mathiesen and Mikayla Jacobs for technical assistance in the laboratory, Brendan J. Furneaux for valuable input to the R script, and the curators of H, TUR, and OULU. The Mycena ITS sequences originating from the specimens deposited in H, TUR, and OULU were produced as part of the Finnish Barcode of Life Project (FinBOL) funded by the Ministry of Environment, Finland (YM23/5512/2013), Otto A Malm's Donationsfond, and the Kone Foundation. We thank the European Commission (grant no. 658849) and the Carlsberg Foundation (grant no. CF18-0809) for grants to C.B. Harder that made this research possible. C.B. Harder was financed by a grant from the Danish Independent Research Fund DFF/FNU 2032-00064B (SapMyc) at the time of writing. Research Funding Carlsbergfondet. Grant Number: CF18-0809 Danish Independent Research Fund. Grant Number: 2032-00064B European Commission. Grant Number: 658849 Ministry of Environment, Finland. Grant Number: YM23/5512/2013Peer reviewedPublisher PD

Managing Taxon Data in FinBIF

The Finnish Biodiversity Information Facility, FinBIF (https://species.fi), has developed its own taxon database. This allows FinBIF taxon specialists to maintain their own, expert-validated view of Finnish species. The database covers national needs and can be rapidly expanded by our own development team. Furthermore, in the database each taxon is given a globally unique persistent URI identifier (https://www.w3.org/TR/uri-clarification), which refers to the taxon concept, not just to the name. The identifier doesn’t change if the taxon concept doesn’t change. We aim to ensure compatibility with checklists from other countries by linking taxon concepts as Linked Data (https://www.w3.org/wiki/LinkedData) — a work started as a part of the Nordic e-Infrastructure Collaboration (NeIC) DeepDive project (https://neic.no/deepdive). The database is used as a basis for observation/specimen searches, e-Learning and identification tools, and it is browsable by users of the FinBIF portal. The data is accessible to everyone under CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0) in machine readable formats. The taxon specialists maintain the taxon data using a web application. Currently, there are 60 specialists. All changes made to the data go live every night. The nightly update interval allows the specialists a grace period to make their changes. Allowing the taxon specialists to modify the taxonomy database themselves leads to some challenges. To maintain the integrity of critical data, such as lists of protected species, we have had to limit what the specialists can do. Changes to critical data is carried out by an administrator. The database has special features for linking observations to the taxonomy. These include hidden species aggregates and tools to override how a certain name used in observations is linked to the taxonomy. Misapplied names remain an unresolved problem. The most precise way to record an observation is to use a taxon concept: Most observations are still recorded using plain names, but it is possible for the observer to pick a concept. Also, when data is published in FinBIF from other information systems, the data providers can link their observations to the concepts using the identifiers of concepts. The ability to use taxon concepts as basis of observations means we have to maintain the concepts over time — a task that may become arduous in the future (Fig. 1). As it stands now, the FinBIF taxon data model — including adjacent classes such as publication, person, image, and endangerment assessments — consists of 260 properties. If the data model were stored in a normalized relational database, there would be approximately 56 tables, which could be difficult to maintain. Keeping track of a complete history of data is difficult in relational databases. Alternatively, we could use document storage to store taxon data. However, there are some difficulties associated with document storages: (1) much work is required to implement a system that does small atomic update operations; (2) batch updates modifying multiple documents usually require writing a script; and (3) they are not ideal for doing searches. We use a document storage for observation data, however, because they are well suited for storing large quantities of complex records. In FinBIF, we have decided to use a triplestore for all small datasets, such as taxon data. More specifically, the data is stored according to the RDF specification (https://www.w3.org/RDF). An RDF Schema defines the allowed properties for each class. Our triplestore implementation is an Oracle relational database with two tables (resource and statement), which gives us the ability to do SQL queries and updates. Doing small atomic updates is easy as only a small subset of the triplets can be updated instead of the entire data entity. Maintaining a complete record of history comes without much effort, as it can be done on an individual triplet level. For performance-critical queries, the taxon data is loaded into an Elasticsearch (https://www.elastic.co) search engine

Species Threat Assessment Tool and Online Result Service in FinBIF

The threat assessment of Finnish species was carried out during 2017–2018 for the third time using International Union for Conservation of Nature (IUCN) criteria (IUCN 2012a, IUCN 2012b, IUCN 2016). The Red List was published in March 2019 (Hyvärinen et al. 2019). In conjunction with the assessment of threatened species, 36,602 species or lower taxa were listed. The information was sufficient for assessment of 22,418 species. It was done by 18 expert groups of different organism groups. The process was coordinated by the Finnish Environment Institute Syke and led by the steering group of the assessment from the Ministry of Environment. For the first time, the Finnish Biodiversity Information Facility FinBIF offered a documentation tool and archive for the threat assessment. The assessment was based on the national checklist of Finnish species coordinated by FinBIF. Many of the expert groups are in active collaboration with FinBIF in maintaining FinBIF’s taxon database and in updating the checklists. Hence, there was a good foundation to build on in developing the cooperation further and deepening the integration of the Red Listing process into FinBIF’s IT infrastructure. The documentation tool of the assessment is implemented in the taxon database of FinBIF (Fig. 1). The Red List data of the 2010 (all species) and 2015 (birds and mammals) assessments are readily available in the tool. The assessor can therefore easily copy and confirm e.g. area of occupancy, extent of occurrence, generation length and habitat of a species, if there is no need for editing. The service offers the possibility to add notes to most of the fields separately and commenting on the assessment by other authorized users. The tool archives the history of all changes. In line with the IUCN instructions, the tool automatically chooses the criteria leading to the highest possible threat category of criteria A-E filled out for each species. However, the assessor confirms the final evaluation. Finally, in several fields, the tool automatically checks the validity of values entered, e.g. criteria, threat category, length of the observation period, causes of threat, and current threat factors. The tool includes necessary fields for back-casting the categories of previous assessments to count the Red List Index. There is also a possibility to add or choose references for the assessment of a certain species in the publications part of the taxon database. Due to linkage through the taxon database, the updated threat categories of each Finnish species are immediately available as additional information of each species introduced in FinBIF. Also for the first time, the results of the threat assessment can be examined online directly after its publication at the Red List online service through FinBIF: https://punainenkirja.laji.fi/en. The online service makes Red List categories and related criteria searchable. Data can be categorized also by habitat, causes of threat, or current threat factors. Due to the ability to conduct searches, the online service supplements the printed book (Hyvärinen et al. 2019), which includes extensive summaries for groups of organisms

Linking Fennoscandian Species of Two Fungal Genera: A test case for linked open data

In Norway, Sweden and Finland, we all have our own taxonomy initiatives, mapping our biodiversity (Lahti and Skarp 2019, Sjödin Skarp 2019, Skarp et al. 2019). Together these countries make up most of Fennoscandia, sharing a large part of the fauna, flora and fungi. It was only natural for us to start cooperating through a Nordic Taxonomy Initiative, sharing expertise and knowledge. Our implementation of Linked Open Data (LOD) is a first step toward automated sharing of information about Fennoscandian species. By linking taxon concepts, we can share observations and facilitate our efforts to combat invasive alien species, as well as assessing conservation status of our native species (Liljeblad and Lahti 2019a, Liljeblad and Lahti 2019b).We picked the fungal genera Hygrophorus Fr. and Tricholoma (Fr.) Staude (Basiciomycetes: Agaricales) as test cases for matching species concepts between our countries. We downloaded lists of species from Checklistbank as a starting point, including synonym names and documenting the specific versions. However, the identifiers for these taxa are not independent of name and concept changes here, so this backbone was imported into taxonid.org. A spreadsheet with these taxa was then complemented with taxa from the three respective countries’ taxonomic databases.In Hygrophorus, there were 35 species with 28 present in Finland, 33 in Norway and 34 in Sweden. The mycologists among us discussed the full list during a virtual workshop and agreed upon how to interpret their respective taxonomies compared to the list at taxonid.org. Next, we copied the identifier for each species in taxonid.org to our respective national databases.Matching up all species of Hygrophorus took about 3 hours for 3 people, making for a total of 9 hours of effort excluding things such as exporting and preparing checklists for comparison. Adding the identifiers from taxonid.org into the respective national databases was a simple import of a maximum one hour each. We then did the same for the more species-rich genus Tricholoma.In the process, besides the links, we have established closer personal contact, synced our views on the taxonomy and had a chance to tidy up the nomenclature. When attempting to share more than taxonomic information, we have come to realize how our countries differ in usage of standard terms documenting residency, reproductive status as well as that of establishment means. For now, we will have to make do with a simple absence/presence, but having the actual taxon links is the prerequisite we are now starting to fulfill