Dataset Information

A specialist's audit of aggregated occurrence records.

ABSTRACT: Occurrence records for named, native Australian millipedes from the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA) were compared with the same records from the Millipedes of Australia (MoA) website, compiled independently by the author. The comparison revealed some previously unnoticed errors in MoA, and a much larger number of errors and other problems in the aggregated datasets. Errors have been corrected in MoA and in some data providers' databases, but will remain in GBIF and ALA until data providers have supplied updates to these aggregators. An audit by a specialist volunteer, as reported here, is not a common occurrence. It is suggested that aggregators should do more, or more effective, data checking and should query data providers when possible errors are detected, rather than simply disclaim responsibility for aggregated content.

SUBMITTER: Mesibov R

PROVIDER: S-EPMC3677402 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A specialist's audit of aggregated occurrence records.

Mesibov Robert R

ZooKeys 20130419 293

Occurrence records for named, native Australian millipedes from the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA) were compared with the same records from the Millipedes of Australia (MoA) website, compiled independently by the author. The comparison revealed some previously unnoticed errors in MoA, and a much larger number of errors and other problems in the aggregated datasets. Errors have been corrected in MoA and in some data providers' databases, bu ...[more]

PMID: 23794864

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Project description:A species' distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr's API. We show the biodiversity database's 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

Dataset Information

A specialist's audit of aggregated occurrence records.

Publications

A specialist's audit of aggregated occurrence records.

Similar Datasets

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