A proposed order-level classification in Oligochaeta (Annelida, Clitellata)

The purpose of our contribution is to propose a robust and practical order-level classification of the families of Oligochaeta, that is, non-leech Clitellata. The order level is mandatory in Linnaean rank-based classification and is also required in many internet-based biodiversity databases. However, it has received little attention in oligochaete systematics, and the few available order-level classifications of Oligochaeta no longer represent phylogenetic relationships adequately. Our proposal is based on corroborated molecular phylogenetic evidence and takes as benchmarks class level for Clitellata, subclass level for Oligochaeta and Hirudinea, and order level for Crassiclitellata, the monophylum that includes most of the earthworm taxa. As a result, eleven orders are proposed: Alluroidida Timm & Martin, 2015; Capilloventrida Timm, n. ordo; Crassiclitellata Jamieson, 1988; Enchytraeida Kasprzak, 1984; Haplotaxida Brinkhurst & Jamieson, 1971; Lumbriculida Brinkhurst & Jamieson, 1971; Moniligastrida Brinkhurst & Jamieson, 1971; Narapida Timm, n. ordo; Parvidrilida Timm, n. ordo; Randiellida Jamieson, 1988; Tubificida Jamieson, 1978. This order-level classification is robust and easily adaptable to future insights into phylogenetic relationships.

An Australian collector's authority file, 1973–2020

Biodiversity databases contain omissions and errors, including those resulting from data entry mistakes and from the use of outdated or incorrect data sources. Some of these omissions and errors can be minimised by the use of authority files, such as expert-compiled taxonomic name databases. However, there are few publicly available authority files for collecting events, and the "where", "when" and "by whom" of specimen data are typically entered into biodiversity databases separately and directly, item by item from specimen labels. Here I describe a publicly available compilation of 3829 of my own collecting events over a 48-year period in Australia. Each record contains a unique combination of date, georeferenced location and location notes.

Measuring the temporal quality of a biodiversity database

In the context of climate change it is important to keep biodiversity databases up-to-date. This priority generates the need for a metric to assess the concept of up-to-dateness. The objective of this paper is to devise a measure of up-to-dateness for atlas-type biodiversity data. The data input into the algorithm consists of the species, date and grid cell allocation of all available records for a taxon in a region. First, for each grid cell in a region, the median of the date of the most recent record of each species is calculated. Secondly, the median of the median dates for each grid cell yields an overall measure of up-to-dateness. The performance of this algorithm is investigated in relation to databases for six taxa in southern Africa. In June 2021, the up-to-dateness of the databases varied from 41 years for the reptile database to two years for the bird database. The quality of a biodiversity database is a multidimensional concept; up-to-dateness is only one of several dimensions. The paper identifies a need to quantify the rate at which the “value” of a record decays as evidence that a species still occurs at a locality, and suggests an experimental process for doing this. The use of the up-to-dateness index to motivate citizen scientists is discussed.

A review of the heterogeneous landscape of biodiversity databases: opportunities and challenges for a synthesized biodiversity knowledge base

Aim: Addressing global environmental challenges requires access to biodiversity data across wide spatial, temporal and biological scales. Recent decades have witnessed an exponential increase of biodiversity information aggregated by biodiversity databases (hereafter ‘databases’). However, heterogeneous coverage, protocols, and standards of databases hampered the data integration among databases. To stimulate the next stage of data integration, here we present a synthesis of major databases, and investigate i) how the coverages of databases vary across taxonomy, space, and record type; ii) the degree of integration among databases; iii) how integration of databases can increase biodiversity knowledge; iv) the barriers to databases integration.Location: GlobalTime period: ContemporaryMajor taxa studied: Plants and VertebratesMethods: We reviewed the scope of twelve well-established databases and assessed the status of their integration. We synthesized information from these databases to assess major knowledge gaps and barriers to fully integration. We estimated how improved integration can increase the coverage and depth of biodiversity knowledge. Results: Each reviewed database had unique focus of data coverages. Data flows were common among databases, though not always clearly documented. Functional trait databases were more isolated than those pertaining to species distributions. Poor compatibility between taxonomic systems used by different databases posed a major challenge to integration. We demonstrated that integration of distribution databases can lead to greater taxonomic coverage that corresponds to 23 years’ advancement in knowledge accumulation, and improvement in taxonomic coverage could be as high as 22.4% for trait databases. Main conclusions: Rapid increase of biodiversity knowledge can be achieved through the integration of databases, providing the data necessary to address critical environmental challenges. Our synthesis provides an overview of the integration status of databases. Full integration across databases will require tackling the major impediments to data integration – taxonomic incompatibility, lags in data exchange, barriers to effective data synchronization, and isolation of individual initiatives.

Avenues into Integration: Communicating taxonomic intelligence from sender to recipient

“What is crucial for your ability to communicate with me… pivots on the recipient’s capacity to interpret—to make good inferential sense of the meanings that the declarer is able to send” (Rescher 2000, p148). Conventional approaches to reconciling taxonomic information in biodiversity databases have been based on string matching for unique taxonomic name combinations (Kindt 2020, Norman et al. 2020). However, in their original context, these names pertain to specific usages or taxonomic concepts, which can subsequently vary for the same name as applied by different authors. Name-based synonym matching is a helpful first step (Guala 2016, Correia et al. 2018), but may still leave considerable ambiguity regarding proper usage (Fig. 1). Therefore, developing "taxonomic intelligence" is the bioinformatic challenge to adequately represent, and subsequently propagate, this complex name/usage interaction across trusted biodiversity data networks. How do we ensure that senders and recipients of biodiversity data not only can share messages but do so with “good inferential sense” of their respective meanings? Key obstacles have involved dealing with the complexity of taxonomic name/usage modifications through time, both in terms of accounting for and digitally representing the long histories of taxonomic change in most lineages. An important critique of proposals to use name-to-usage relationships for data aggregation has been the difficulty of scaling them up to reach comprehensive coverage, in contrast to name-based global taxonomic hierarchies (Bisby 2011). The Linnaean system of nomenclature has some unfortunate design limitations in this regard, in that taxonomic names are not unique identifiers, their meanings may change over time, and the names as a string of characters do not encode their proper usage, i.e., the name “Genus species” does not specify a source defining how to use the name correctly (Remsen 2016, Sterner and Franz 2017). In practice, many people provide taxonomic names in their datasets or publications but not a source specifying a usage. The information needed to map the relationships between names and usages in taxonomic monographs or revisions is typically not presented it in a machine-readable format. New approaches are making progress on these obstacles. Theoretical advances in the representation of taxonomic intelligence have made it increasingly possible to implement efficient querying and reasoning methods on name-usage relationships (Chen et al. 2014, Chawuthai et al. 2016, Franz et al. 2015). Perhaps most importantly, growing efforts to produce name-usage mappings on a medium scale by data providers and taxonomic authorities suggest an all-or-nothing approach is not required. Multiple high-profile biodiversity databases have implemented internal tools for explicitly tracking conflicting or dynamic taxonomic classifications, including eBird using concept relationships from AviBase (Lepage et al. 2014); NatureServe in its Biotics database; iNaturalist using its taxon framework (Loarie 2020); and the UNITE database for fungi (Nilsson et al. 2019). Other ongoing projects incorporating taxonomic intelligence include the Flora of Alaska (Flora of Alaska 2020), the Mammal Diversity Database (Mammal Diversity Database 2020) and PollardBase for butterfly population monitoring (Campbell et al. 2020).

Towards a catalogue of biodiversity databases: An ontological case study

Biodiversity informatics depends on digital access to credible information about species. Many online resources host species’ data, but the lack of categorisation for these resources inhibits the growth of this entire field. To explore possible solutions, we examined the (now retired) Biodiversity Information Projects of the World (BIPW) dataset created by the Biodiversity Information Standards (TDWG); this project, which ran from 2007-2015 (officially removed from the TDWG website in 2018) was an attempt at organising the Web's biodiversity databases into an indexed list. To do this, we applied a simple classification scheme to score databases within BIPW based on nine data categories, to characterise trends and current compositions of this biodiversity e-infrastructure. Primarily, we found that of 600 databases investigated from BIPW, only 315 (~53%) were accessible at the time of this writing, underscoring the precarious nature of the biodiversity information landscape. Many of these databases are still available, but suffer accessibility issues such as link rot, thus putting the information they contain in danger of being lost. We propose that a community-driven database of biodiversity databases with an accompanying ontology could facilitate efficient discovery of relevant biodiversity databases and support smaller databases – which have the greatest risk of being lost.