A journey through time: exploring temporal patterns amongst digitized plant specimens from Australia. Issue 6 (18th August 2018)

• Record Type: Journal Article
• Authors: Haque, MD. Mohasinul; Nipperess, David A.; Baumgartner, John B.; Beaumont, Linda J.
• Is Part Of: Systematics and biodiversity. Volume 16:Issue 6(2018)
• Page Start: 604
• Page End: 613
• Publication Date: 2018-08-18
• Subjects: data quality -- natural history collections -- species occurrence data -- specimen records -- temporal bias -- temporal consistency; Biodiversity -- Periodicals; Biology -- Classification -- Periodicals; Natural history -- Periodicals; Biodiversity; Biology; Classification; Periodicals; 578
• DOI: 10.1080/14772000.2018.1472674
• Languages: English
• ISSNs: 1478-0933

Abstract

Abstract : Online access to species occurrence records has opened new windows into investigating biodiversity patterns across multiple scales. The value of these records for research depends on their spatial, temporal, and taxonomic quality. We assessed temporal patterns in records from the Australasian Virtual Herbarium, asking: (1) How temporally consistent has collecting been across Australia? (2) Which areas of Australia have the most reliable records, in terms of temporal consistency and inventory completeness? (3) Are there temporal trends in the completeness of attribute information associated with records? We undertook a multi-step filtering procedure, then estimated temporal consistency and inventory completeness for sampling units (SUs) of 50 km × 50 km. We found temporal bias in collecting, with 80% of records collected over the period 1970–1999. South-eastern Australia, the Wet Tropics in north-east Queensland, and parts of Western Australia have received the most consistent sampling effort over time, whereas much of central Australia has had low temporal consistency. Of the SUs, 18% have relatively complete inventories with high temporal consistency in sampling. We also determined that 25% of digitized records had missing attribute information. By identifying areas with low reliability, we can limit erroneous inferences about distribution patterns and identify priority areas for future sampling.