scholarly journals Using the Taxonomic Backbone(s): The challenge of selecting a taxonomic resource and integrating it with a collection management solution

Author(s):  
Teresa Mayfield-Meyer ◽  
Phyllis Sharp ◽  
Dusty McDonald

The reality is that there is no single “taxonomic backbone”, there are many: the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy, the World Register of Marine Species (WoRMS) and MolluscaBase, to name a few. We could view each one of these as a vertebra on the taxonomic backbone, but even that isn’t quite correct as some of these are nested within others (MolluscaBase contributes to WoRMS, which contributes to Catalogue of Life, which contributes to the GBIF Backbone Taxonomy). How is a collection manager without expertise in a given set of taxa and a limited amount of time devoted to finding the “most current” taxonomy supposed to maintain research grade identifications when there are so many seemingly authoritative taxonomic resources? And once a resource is chosen, how can they seamlessly use the information in that resource? This presentation will document how the Arctos community’s use of the taxon name matching service Global Names Architecture (GNA) led one volunteer team leader in a marine invertebrate collection to attempt to make use of WoRMS taxonomy and how her persistence brought better identifications and classifications to a community of collections. It will also provide insight into some of the technical and curatorial challenges involved in using an outside resource as well as the ongoing struggle to keep up with changes as they occur in the curated resource.

2019 ◽  
Vol 7 ◽  
Author(s):  
Valéria da Silva ◽  
Manoel Aguiar-Neto ◽  
Dan Teixeira ◽  
Cleverson Santos ◽  
Marcos de Sousa ◽  
...  

We present a dataset with information from the Opiliones collection of the Museu Paraense Emílio Goeldi, Northern Brazil. This collection currently has 6,400 specimens distributed in 13 families, 30 genera and 32 species and holotypes of four species: Imeri ajuba Coronato-Ribeiro, Pinto-da-Rocha & Rheims, 2013, Phareicranaus patauateua Pinto-da-Rocha & Bonaldo, 2011, Protimesius trocaraincola Pinto-da-Rocha, 1997 and Sickesia tremembe Pinto-da-Rocha & Carvalho, 2009. The material of the collection is exclusive from Brazil, mostly from the Amazon Region. The dataset is now available for public consultation on the Sistema de Informação sobre a Biodiversidade Brasileira (SiBBr) (https://ipt.sibbr.gov.br/goeldi/resource?r=museuparaenseemiliogoeldi-collection-aracnologiaopiliones). SiBBr is the Brazilian Biodiversity Information System, an initiative of the government and the Brazilian node of the Global Biodiversity Information Facility (GBIF), which aims to consolidate and make primary biodiversity data available on a platform (Dias et al. 2017). Harvestmen or Opiliones constitute the third largest arachnid order, with approximately 6,500 described species. Brazil is the holder of the greatest diversity in the world, with more than 1,000 described species, 95% (960 species) of which are endemic to the country. Of these, 32 species were identified and deposited in the collection of the Museu Paraense Emílio Goeldi.


Author(s):  
Elspeth Haston ◽  
Lorna Mitchell

The specimens held in natural history collections around the world are the direct result of the effort of thousands of people over hundreds of years. However, the way that the names of these people have been recorded within the collections has never been fully standardised, and this makes the process of correctly assigning the event relating to the specimen to an individual difficult at best, and impossible at worst. The events in which people are related to specimens include collecting, identifying, naming, loaning and owning. Whilst there are resources in the botanical community that hold information on many collectors and authors of plant names, the residual number of unknown people and the effort required to disambiguate them is daunting. Moreover, in many cases, the work carried out within the collection to disambiguate the names relating to the specimens is often not recorded and made available, generally due to the lack of a system to do so. This situation is making it extremely difficult to search for collections within the main aggregators, such as GBIF —the Global Biodiversity Information Facility— , and severely hampers our ability to link collections both within and between institutes and disciplines. When we look at benefits of linking collections and people, the need to agree and implement a system of managing people names becomes increasingly urgent.


2020 ◽  
Vol 8 ◽  
Author(s):  
Sonia Ferreira ◽  
Rui Andrade ◽  
Ana Gonçalves ◽  
Pedro Sousa ◽  
Joana Paupério ◽  
...  

The InBIO Barcoding Initiative (IBI) Diptera 01 dataset contains records of 203 specimens of Diptera. All specimens have been morphologically identified to species level, and belong to 154 species in total. The species represented in this dataset correspond to about 10% of continental Portugal dipteran species diversity. All specimens were collected north of the Tagus river in Portugal. Sampling took place from 2014 to 2018, and specimens are deposited in the IBI collection at CIBIO, Research Center in Biodiversity and Genetic Resources. This dataset contributes to the knowledge on the DNA barcodes and distribution of 154 species of Diptera from Portugal and is the first of the planned IBI database public releases, which will make available genetic and distribution data for a series of taxa. All specimens have their DNA barcodes made publicly available in the Barcode of Life Data System (BOLD) online database and the distribution dataset can be freely accessed through the Global Biodiversity Information Facility (GBIF).


Author(s):  
Amy Davis ◽  
Tim Adriaens ◽  
Rozemien De Troch ◽  
Peter Desmet ◽  
Quentin Groom ◽  
...  

To support invasive alien species risk assessments, the Tracking Invasive Alien Species (TrIAS) project has developed an automated, open, workflow incorporating state-of-the-art species distribution modelling practices to create risk maps using the open source language R. It is based on Global Biodiversity Information Facility (GBIF) data and openly published environmental data layers characterizing climate and land cover. Our workflow requires only a species name and generates an ensemble of machine-learning algorithms (Random Forest, Boosted Regression Trees, K-Nearest Neighbors and AdaBoost) stacked together as a meta-model to produce the final risk map at 1 km2 resolution (Fig. 1). Risk maps are generated automatically for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission scenarios and are accompanied by maps illustrating the confidence of each individual prediction across space, thus enabling the intuitive visualization and understanding of how the confidence of the model varies across space and scenario (Fig. 2). The effects of sampling bias are accounted for by providing options to: use the sampling effort of the higher taxon the modelled species belongs to (e.g., vascular plants), and to thin species occurrences. use the sampling effort of the higher taxon the modelled species belongs to (e.g., vascular plants), and to thin species occurrences. The risk maps generated by our workflow are defensible and repeatable and provide forecasts of alien species distributions under further climate change scenarios. They can be used to support risk assessments and guide surveillance efforts on alien species in Europe. The detailied modeling framework and code are available on GitHub: https://github.com/trias-project.


2013 ◽  
Vol 64 (2) ◽  
Author(s):  
Shakina Mohd Talkah ◽  
Iylia Zulkiflee ◽  
Mohd Shahir Shamsir

Currently, all the information regarding ethnobotanical, phytochemical and pharmaceutical information of South East Asia are scattered over many different publications, depositories and databases using various digital and analogue formats. Although there are taxonomic databases of medicinal plants, they are not linked to phytochemical and pharmaceutical information which are often resides in scientific literature. We present Phyknome; an ethnobotanical and phytochemical database with more than 22,000 species of ethnoflora of Asia. The creation of this database will enable a biotechnology researcher to seek and identify ethnobotanical information based on a species’ scientific name, description and phytochemical information. It is constructed using a digitization pipeline that allow high throughput digitization of archival data, an automated dataminer to mine for pharmaceutical compounds information and an online database to integrated these information. The main functions include an automated taxonomy, bibliography and API interface with primary databases such as Global Biodiversity Information Facility (GBIF). We believe that Phyknome will contribute to the digital knowledge ecosystem to elevate access and provide tools for ethnobotanical research and contributes to the management, assessment and stewardship of biodiversity. The database is available at http://mapping.fbb.utm.my/phyknome/.


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