scholarly journals Do Fabaceae species with physical dormancy occur mostly in the temperate ecosystems? A rebuttal to using global biodiversity information facility (GBIF) analysis

2020 ◽  
Vol 7 (1) ◽  
pp. 109-111
Author(s):  
Ganesh K. Jaganathan
Keyword(s):  
Distribution Models ◽  
Physical Dormancy ◽  
Global Biodiversity Information Facility ◽  
Tropical Ecosystem ◽  
Policy Makers ◽  
Animal Distribution ◽  
Tropical Vegetation ◽  
Global Biodiversity ◽  
Seed Coats ◽  
Biodiversity Information

Physical dormancy (PY) is a phenomenon wherein seed coats are impermeable to water. This feature prevents immediate germination in seeds, therefore considered as an adaptive trait in species of Mediterranean and tropical ecosystem, where rainy season is the most favorable time for germination. However, using dataset available for Fabaceae collected from global biodiversity information facility (GBIF), the largest family with PY, recent studies have provided evidence contrasting this assertion. This viewpoint has arisen owing to the fact that the data were gleaned by georeferencing the Fabaceae species distribution from GBIF, which is under-represented for the tropical vegetation. This is similar to other reports available in other plant and animal distribution models, where GBIF data is not an accurate representation of distribution. A closer inspection of the data available in literature suggests that using GBIF database alone to map the distribution of Fabaceae species represents the extreme end of biased data causing misperception and could mislead the scientific community, particularly ecologists, conservationists and/or policy makers.

scholarly journals Towards a Post-Graduate Level Curriculum for Biodiversity Informatics. Perspectives from the Global Biodiversity Information Facility (GBIF) Community

2021 ◽  
Vol 9 ◽  
Author(s):  
Fatima Parker-Allie ◽  
Francisco Pando ◽  
Anders Telenius ◽  
Jean Ganglo ◽  
Danny Vélez ◽  
...  
Keyword(s):  
Biological Data ◽  
Initial Assessment ◽  
Biodiversity Informatics ◽  
Global Biodiversity Information Facility ◽  
Policy Makers ◽  
Academic Teaching ◽  
E Learning ◽  
Learning Platforms ◽  
Global Biodiversity ◽  
Biodiversity Information

Biodiversity informatics is a new and evolving field, requiring efforts to develop capacity and a curriculum for this field of science. The main objective was to summarise the level of activity and the efforts towards developing biodiversity informatics curricula, for work-based training and/or academic teaching at universities, taking place within the Global Biodiversity Information Facility (GBIF) countries and its associated network. A survey approach was used to identify existing capacities and resources within the network. Most of GBIF Nodes survey respondents (80%) are engaged in onsite training activities, with a focus on work-based professionals, mostly researchers, policy-makers and students. Training topics include data mobilisation, digitisation, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data that currently reside as scattered datasets. An initial assessment of academic teaching activities highlighted that countries in most regions, to varying degrees, were already engaged in the conceptualisation, development and/or implementation of formal academic programmes in biodiversity informatics, including programmes in Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. Digital e-learning platforms were an important tool to help build capacity in many countries. In terms of the potential in the Nodes network, 60% expressed willingness to be recruited or commissioned for capacity enhancement purposes. Contributions and activities of various country nodes across the network have been highlighted and a working curriculum framework has been defined.

scholarly journals The InBIO Barcoding Initiative Database: DNA barcodes of Portuguese Diptera 01

2020 ◽  
Vol 8 ◽  
Author(s):  
Sonia Ferreira ◽  
Rui Andrade ◽  
Ana Gonçalves ◽  
Pedro Sousa ◽  
Joana Paupério ◽  
...  
Keyword(s):  
Species Level ◽  
Distribution Data ◽  
Dna Barcodes ◽  
Online Database ◽  
Global Biodiversity Information Facility ◽  
Life Data ◽  
Tagus River ◽  
Global Biodiversity ◽  
Biodiversity Information ◽  
Dipteran Species

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).

scholarly journals Using Global Biodiversity Information Facility Occurrence Data for Automated Invasive Alien Species Risk Mapping 

2020 ◽  
Vol 4 ◽  
Author(s):  
Amy Davis ◽  
Tim Adriaens ◽  
Rozemien De Troch ◽  
Peter Desmet ◽  
Quentin Groom ◽  
...  
Keyword(s):  
Alien Species ◽  
Vascular Plants ◽  
Invasive Alien Species ◽  
Risk Assessments ◽  
Sampling Effort ◽  
Global Biodiversity Information Facility ◽  
Global Biodiversity ◽  
Risk Maps ◽  
Species Occurrences ◽  
Biodiversity Information

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.

Development and Deployment of an Ethnobotanical and Phytochemical Knowledge Database of Malaysia

2013 ◽  
Vol 64 (2) ◽  
Author(s):  
Shakina Mohd Talkah ◽  
Iylia Zulkiflee ◽  
Mohd Shahir Shamsir
Keyword(s):  
Scientific Literature ◽  
Pharmaceutical Compounds ◽  
South East Asia ◽  
Online Database ◽  
Global Biodiversity Information Facility ◽  
Knowledge Database ◽  
Global Biodiversity ◽  
Digital Knowledge ◽  
Biodiversity Information ◽  
Scientific Name

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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