scholarly journals Current GBIF occurrence data demonstrates both promise and limitations for potential red listing of spiders

2019 ◽  
Vol 7 ◽  
Author(s):  
Vaughn Shirey ◽  
Sini Seppälä ◽  
Vasco Branco ◽  
Pedro Cardoso
Keyword(s):  
Iucn Red List ◽  
Red List ◽  
Global Biodiversity Information Facility ◽  
Occurrence Data ◽  
Primary Literature ◽  
Assessment Metrics ◽  
Conservation Assessments ◽  
Source Of Information ◽  
Biodiversity Information ◽  
Combined Data

Conservation assessments of hyperdiverse groups of organisms are often challenging and limited by the availability of occurrence data needed to calculate assessment metrics such as extent of occurrence (EOO). Spiders represent one such diverse group and have historically been assessed using primary literature with retrospective georeferencing. Here we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List classifications for two extensive spider datasets comprising 479 species in total. The EOO were estimated and compared using literature-based assessments, Global Biodiversity Information Facility (GBIF)-based assessments and combined data assessments. We found that although few changes to hypothetical IUCN Red List classifications occurred with the addition of GBIF data, some species (3.3%) which could previously not be classified could now be assessed with the addition of GBIF data. In addition, the hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone did not provide enough data for 88.7% of species. These results demonstrate the potential of GBIF data to serve as an additional source of information for conservation assessments, complementing literature data, but not particularly useful on its own as it stands right now for spiders.

scholarly journals Rapid Least Concern: towards automating Red List assessments

2020 ◽  
Vol 8 ◽  
Author(s):  
Steven Bachman ◽  
Barnaby Walker ◽  
Sara Barrios ◽  
Alison Copeland ◽  
Justin Moat
Keyword(s):  
Web Application ◽  
Native Species ◽  
Single Species ◽  
Iucn Red List ◽  
Red List ◽  
Global Biodiversity Information Facility ◽  
Batch Operation ◽  
Occurrence Data ◽  
Data Files ◽  
Data Requirements

The IUCN Red List of Threatened SpeciesTM (hereafter the Red List) is an important global resource for conservation that supports conservation planning, safeguarding critical habitat and monitoring biodiversity change (Rodrigues et al. 2006). However, a major shortcoming of the Red List is that most of the world's described species have not yet been assessed and published on the Red List (Bachman et al. 2019Eisenhauer et al. 2019). Conservation efforts can be better supported if the Red List is expanded to achieve greater coverage of mega-diverse groups of organisms such as plants, fungi and invertebrates. There is, therefore, an urgent need to speed up the Red List assessment and documentation workflow. One reason for this lack of species coverage is that a manual and relatively time-consuming procedure is usually employed to assess and document species. A recent update of Red List documentation standards (IUCN 2013) reduced the data requirements for publishing non-threatened or 'Least Concern' species on the Red List. The majority of the required fields for Least Concern plant species can be found in existing open-access data sources or can be easily calculated. There is an opportunity to consolidate these data and analyses into a simple application to fast-track the publication of Least Concern assessments for plants. There could be as many as 250,000 species of plants (60%) likely to be categorised as Least Concern (Bachman et al. 2019), for which automatically generated assessments could considerably reduce the outlay of time and valuable resources for Red Listing, allowing attention and resources to be dedicated to the assessment of those species most likely to be threatened. We present a web application, Rapid Least Concern, that addresses the challenge of accelerating the generation and documentation of Least Concern Red List assessments. Rapid Least Concern utilises open-source datasets, such as the Global Biodiversity Information Facility (GBIF) and Plants of the World Online (POWO) through a simple web interface. Initially, the application is intended for use on plants, but it could be extended to other groups, depending on the availability of equivalent datasets for these groups. Rapid Least Concern users can assess a single species or upload a list of species that are assessed in a batch operation. The batch operation can either utilise georeferenced occurrence data from GBIF or occurrence data provided by the user. The output includes a series of CSV files and a point map file that meet the minimum data requirements for a Least Concern Red List assessment (IUCN 2013). The CSV files are compliant with the IUCN Red List SIS Connect system that transfers the data files to the IUCN database and, pending quality control checks and review, publication on the Red List. We outline the knowledge gap this application aims to fill and describe how the application works. We demonstrate a use-case for Rapid Least Concern as part of an ongoing initiative to complete a global Red List assessment of all native species for the United Kingdom Overseas Territory of Bermuda.

scholarly journals Using publicly available data to conduct rapid assessments of extinction risk

2020 ◽  
Author(s):  
Michael O Levin ◽  
Jared B Meek ◽  
Brian Boom ◽  
Sara M Kross ◽  
Evan A Eskew
Keyword(s):  
Plant Species ◽  
Extinction Risk ◽  
Iucn Red List ◽  
Red List ◽  
Assessment Process ◽  
Conservation Priorities ◽  
Global Biodiversity Information Facility ◽  
Red List Species ◽  
Data Deficient ◽  
Biodiversity Information

The IUCN Red List plays a key role in setting global conservation priorities. Species are added to the Red List through a rigorous assessment process that, while robust, can be quite time-intensive. Here, we test the rapid preliminary assessment of plant species extinction risk using a single Red List metric: Extent of Occurrence (EOO). To do so, we developed REBA (Rapid EOO-Based Assessment), a workflow that harvests and cleans data from the Global Biodiversity Information Facility (GBIF), calculates each species' EOO, and assigns Red List categories based on that metric. We validated REBA results against 1,546 North American plant species already on the Red List and found ~90% overlap between REBA's rapid classifications and those of full IUCN assessments. Our preliminary workflow can be used to quickly evaluate data deficient Red List species or those in need of reassessment, and can prioritize unevaluated species for a full assessment.

scholarly journals Red List assessments of East African chameleons: a case study of why we need experts

Oryx ◽  
2014 ◽  
Vol 49 (4) ◽  
pp. 652-658 ◽  
Author(s):  
Angelique Hjarding ◽  
Krystal A. Tolley ◽  
Neil D. Burgess
Keyword(s):  
Geographical Distribution ◽  
Conservation Status ◽  
Iucn Red List ◽  
Red List ◽  
East African ◽  
Global Biodiversity Information Facility ◽  
Extra Step ◽  
Using Data ◽  
Threatened Status ◽  
Expert Data

AbstractThe IUCN Red List of Threatened Species uses geographical distribution as a key criterion in assessing the conservation status of species. Accurate knowledge of a species’ distribution is therefore essential to ensure the correct categorization is applied. Here we compare the geographical distribution of 35 species of chameleons endemic to East Africa, using data from the Global Biodiversity Information Facility (GBIF) and data compiled by a taxonomic expert. Data screening showed 99.9% of GBIF records used outdated taxonomy and 20% had no locality coordinates. Conversely the expert dataset used 100% up-to-date taxonomy and only seven records (3%) had no coordinates. Both datasets were used to generate range maps for each species, which were then used in preliminary Red List categorization. There was disparity in the categories of 10 species, with eight being assigned a lower threat category based on GBIF data compared with expert data, and the other two assigned a higher category. Our results suggest that before conducting desktop assessments of the threatened status of species, aggregated museum locality data should be vetted against current taxonomy and localities should be verified. We conclude that available online databases are not an adequate substitute for taxonomic experts in assessing the threatened status of species and that Red List assessments may be compromised unless this extra step of verification is carried out.

scholarly journals A review of Dendrocephalus (Dendrocephalinus) (Crustacea: Anostraca) with the first records of male-male anostracan aggressive competition

2019 ◽  
Author(s):  
D. Christopher Rogers ◽  
Ann Dunn ◽  
W. Wayne Price
Keyword(s):  
New Species ◽  
New Records ◽  
Iucn Red List ◽  
Red List ◽  
Agonistic Behaviour ◽  
Conservation Assessments ◽  
A New Species

We present a review of Dendrocephalus (Dendrocephalinus) with an updated diagnosis for the subgenus and a key to all known species. We provide new records of Dendrocephalus alachua, which was previously supposed to be extinct, and we describe a new species, Dendrocephalus proeliator sp. nov., which is separated from all other species based on the form of the male frontal appendage. Dendrocephalus proeliator sp. nov. appears to be morphologically intermediate between D. alachua and D. lithaca. In addition, we provide conservation assessments for all four species in the subgenus, according to IUCN Red List standards. We also report for two species the first known examples of direct male-male agonistic behaviour and competition for access to areas frequented by receptive females.

scholarly journals R Python, and Ruby clients for GBIF species occurrence data

2017 ◽  
Author(s):  
Scott A Chamberlain ◽  
Carl Boettiger
Keyword(s):  
Programming Languages ◽  
Significant Contribution ◽  
Species Occurrence ◽  
Global Biodiversity Information Facility ◽  
Occurrence Data ◽  
Research Questions ◽  
Global Biodiversity ◽  
Number Of Individuals ◽  
Biodiversity Information ◽  
Programmatic Access

Background. The number of individuals of each species in a given location forms the basis for many sub-fields of ecology and evolution. Data on individuals, including which species, and where they're found can be used for a large number of research questions. Global Biodiversity Information Facility (hereafter, GBIF) is the largest of these. Programmatic clients for GBIF would make research dealing with GBIF data much easier and more reproducible. Methods. We have developed clients to access GBIF data for each of the R, Python, and Ruby programming languages: rgbif, pygbif, gbifrb. Results. For all clients we describe their design and utility, and demonstrate some use cases. Discussion. Programmatic access to GBIF will facilitate more open and reproducible science - the three GBIF clients described herein are a significant contribution towards this goal.

scholarly journals What You Probably Didn't Know about Biodiversity Information Serving Our Nation (BISON) 

2019 ◽  
Vol 3 ◽  
Author(s):  
Gerald Guala
Keyword(s):  
The United States ◽  
Species Occurrence ◽  
Global Biodiversity Information Facility ◽  
Exact Role ◽  
Occurrence Data ◽  
The Us ◽  
Significant Augmentation ◽  
The Relationship ◽  
Biodiversity Information

Biodiversity Information Serving Our Nation (BISON - bison.usgs.gov) is the US Node application for the Global Biodiversity Information Facility (GBIF) and the most comprehensive source of species occurrence data for the United States of America. It currently contains more than 460 million records and provides significant augmentation and integration of US occurrence data in terrestrial, marine and freshwater systems. Publicly released in 2013, BISON has generated a large community of stakeholders and they have passed on a lot of questions over the years through email ([email protected]), presentations and other means. In this presentation, some of the most common questions will be addressed in detail. For example: why all BISON data isn't in GBIF; how is BISON different from GBIF; what is the relationship between BISON and other US providers to GBIF; and what is the exact role of the Integrated Taxonomic Information System (ITIS - www.itis.gov) in BISON.

scholarly journals BioNames: linking taxonomy, texts, and trees

2013 ◽  
Author(s):  
Roderic D M Page
Keyword(s):  
Phylogenetic Trees ◽  
Basic Question ◽  
Web Database ◽  
Multiple Sources ◽  
Global Biodiversity Information Facility ◽  
Phylogenetic Information ◽  
Bibliographic Data ◽  
Primary Literature ◽  
Animal Names ◽  
Biodiversity Information

BioNames is a web database of taxonomic names for animals, linked to the primary literature and, wherever possible, to phylogenetic trees. It aims to provide a taxonomic "dashboard" where at a glance we can see a summary of the taxonomic and phylogenetic information we have for a given taxon and hence provide a quick answer to the basic question "what is this taxon?" BioNames combines classifications from the Global Biodiversity Information Facility (GBIF) and GenBank, imagery from the Encyclopedia of Life (EOL), animal names from the Index of Organism Names (ION), and bibliographic data from multiple sources including the Biodiversity Heritage Library (BHL) and CrossRef. The user interface includes display of full text articles, interactive timelines of taxonomic publications, and zoomable phylogenies. It is available at http://bionames.org.

scholarly journals BioNames: linking taxonomy, texts, and trees

2013 ◽  
Author(s):  
Roderic D M Page
Keyword(s):  
Phylogenetic Trees ◽  
Basic Question ◽  
Web Database ◽  
Multiple Sources ◽  
Global Biodiversity Information Facility ◽  
Phylogenetic Information ◽  
Bibliographic Data ◽  
Primary Literature ◽  
Animal Names ◽  
Biodiversity Information

BioNames is a web database of taxonomic names for animals, linked to the primary literature and, wherever possible, to phylogenetic trees. It aims to provide a taxonomic "dashboard" where at a glance we can see a summary of the taxonomic and phylogenetic information we have for a given taxon and hence provide a quick answer to the basic question "what is this taxon?" BioNames combines classifications from the Global Biodiversity Information Facility (GBIF) and GenBank, imagery from the Encyclopedia of Life (EOL), animal names from the Index of Organism Names (ION), and bibliographic data from multiple sources including the Biodiversity Heritage Library (BHL) and CrossRef. The user interface includes display of full text articles, interactive timelines of taxonomic publications, and zoomable phylogenies. It is available at http://bionames.org.

scholarly journals Automated conservation assessment of the orchid family using deep learning

2020 ◽  
Author(s):  
Alexander Zizka ◽  
Daniele Silvestro ◽  
Pati Vitt ◽  
Tiffany M. Knight
Keyword(s):  
Sampling Bias ◽  
Iucn Red List ◽  
Oceanic Islands ◽  
Red List ◽  
Data Availability ◽  
Conservation Assessment ◽  
Tropical Plant ◽  
Evaluation Bias ◽  
Orchid Conservation ◽  
Conservation Assessments

AbstractIUCN Red List assessments are essential for prioritizing conservation needs but are resource-intensive and therefore only available for a fraction of global species richness. Tropical plant species are particularly under-represented on the IUCN Red List. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. Here, we present automated conservation assessments for 13,910 species of the diverse and globally distributed Orchid family (Orchidaceae), based on a novel method using a deep neural network (IUC-NN), most of which (13,049) were previously unassessed by the IUCN Red List. We identified 4,342 (31.2 % of the evaluated orchid species) as Possibly Threatened with extinction (equivalent to the IUCN categories CR, EN, or VU) and point to Madagascar, East Africa, south-east Asia, and several oceanic islands as priority areas for orchid conservation. Furthermore, the Orchid family provides a model, to test the sensitivity of automated assessment methods to issues with data availability, data quality and geographic sampling bias. IUC-NN identified threat-ened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias compared to the IUCN Red List, and was robust against low data availability and geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in achieving goals of identifying the species that are at greatest risk of extinction.

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