Data from: Horses in the Cloud: big data exploration and mining of fossil and extant Equus (Mammalia: Equidae)
MacFadden, Bruce J.; Guralnick, Robert P. (2016), Data from: Horses in the Cloud: big data exploration and mining of fossil and extant Equus (Mammalia: Equidae), Dryad, Dataset, https://doi.org/10.5061/dryad.qc2fm
Extant species of the genus Equus (e.g., horses, asses, and zebras) have a widespread distribution today on all continents except Antarctica. Extinct species of Equus represented by fossils were likewise widely distributed in the Pliocene and even more so during the Pleistocene. In order to understand the efficacy of “big data” for (paleo)biogeographic analyses, location records (latitude, longitude) and fossil occurrences for the genus Equus were mined and further explored from six databases, including iDigBio, Paleobiology Database, VertNet, BISON, Neotoma, and GBIF. These were chosen from a priori knowledge of where relevant data might be aggregated. We also realized that these databases have different objectives and data sources and therefore would provide a useful comparative study of the widespread taxon Equus in space and time. The mining of Equus data from these six sources yielded a combined total of 123.8 K location records, including 116.2K fossil specimens. These include individual points that are unique, that is, only occurring in one of these databases, and those that are duplicated in multiple databases. Of the six databases, three (iDigBio, Paleobiology Database, and GBIF) were judged to be the most useful in the Equus use case. Most of the databases are biased toward North American records, thus limiting the reconstruction of the actual distribution of the genus Equus in space and time outside of this continent. Although Equus has a large number of digitally accessible records, fundamentally interesting questions pertaining to evolutionary dynamics and extinction geography are still a challenge for these kinds of biodiversity databases due primarily to the lack of sufficiently dense and precise temporal data.
National Science Foundation, Award: EF 1115210, DBI 1547229