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An audit of some processing effects in aggregated occurrence records.

ABSTRACT: A total of ca 800,000 occurrence records from the Australian Museum (AM), Museums Victoria (MV) and the New Zealand Arthropod Collection (NZAC) were audited for changes in selected Darwin Core fields after processing by the Atlas of Living Australia (ALA; for AM and MV records) and the Global Biodiversity Information Facility (GBIF; for AM, MV and NZAC records). Formal taxon names in the genus- and species-groups were changed in 13-21% of AM and MV records, depending on dataset and aggregator. There was little agreement between the two aggregators on processed names, with names changed in two to three times as many records by one aggregator alone compared to records with names changed by both aggregators. The type status of specimen records did not change with name changes, resulting in confusion as to the name with which a type was associated. Data losses of up to 100% were found after processing in some fields, apparently due to programming errors. The taxonomic usefulness of occurrence records could be improved if aggregators included both original and the processed taxonomic data items for each record. It is recommended that end-users check original and processed records for data loss and name replacements after processing by aggregators.

SUBMITTER: Mesibov R

PROVIDER: S-EPMC5923217 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An audit of some processing effects in aggregated occurrence records.

Mesibov Robert R

ZooKeys 20180420 751

A total of ca 800,000 occurrence records from the Australian Museum (AM), Museums Victoria (MV) and the New Zealand Arthropod Collection (NZAC) were audited for changes in selected Darwin Core fields after processing by the Atlas of Living Australia (ALA; for AM and MV records) and the Global Biodiversity Information Facility (GBIF; for AM, MV and NZAC records). Formal taxon names in the genus- and species-groups were changed in 13-21% of AM and MV records, depending on dataset and aggregator. T ...[more]

PMID: 29713234

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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

An audit of some processing effects in aggregated occurrence records.

Publications

An audit of some processing effects in aggregated occurrence records.

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