Abstract
Distributions of taxa across time and space are central to understanding biodiversity and biotic change, yet currently available occurrence data, drawn from biodiversity specimen records and observational datasets, are often insufficient to answer many driving questions. Records of “associated taxa,” taxa co-occurring with a specimen at the time and place of collection, have the potential to fill data gaps and expand the spatiotemporal scope of current occurrence records. I developed a method to extract associated taxon records from 84,328 digitized specimen records and examined the potential of these data to improve the quantity and quality of existing species occurrence data. Adding associated taxon records increased the size of the test dataset by 18.5%, spanned multiple decades (1937–2016), and potentially extended the known range of 217 taxa in Florida and up to 1500 taxa in the United States, demonstrating the capacity of these records to deepen our understanding of changes in the distributions of taxa on Earth. These results suggest that increased attention to documenting associated taxa could be a promising way to maximize the impact of every collecting event.
Highlights
In this era of anthropogenic influence, the need to understand past and present species distributions to track biotic change has never been greater
After data cleaning and both duplicate removal steps, 13,372 associated taxon records were extracted from the initial dataset of 72,120 unique herbarium specimen records, representing an 18.5% increase in total occurrence records (Fig. 2)
Increasing our understanding of species distributions is crucial to many scientific aims, including assessing the impact of anthropogenic effects such as climate and land use changes
Summary
In this era of anthropogenic influence, the need to understand past and present species distributions to track biotic change has never been greater. Even en masse, specimen data can be incomplete and geographically, temporally, or taxonomically biased, especially in understudied regions (Tobler et al 2007; Stropp et al 2016; Daru et al 2017). Observational occurrence datasets such as those aggregated by the Global Biodiversity Information Facility (gbif.org) and iNaturalist (inaturalist.org) are rapidly expanding our knowledge of species distributions, but because historical records are often rare, observational datasets often cannot answer essential questions such as how species distributions may shift in time and space with changes in climate and land use
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