Spatial standardization of taxon occurrence data—a call to action

Abstract

The fossil record is spatiotemporally heterogeneous: taxon occurrence data have patchy spatial distributions, and this patchiness varies through time. Inferences from large-scale quantitative paleobiology studies that fail to account for heterogeneous sampling coverage will be uninformative at best and confidently wrong at worst. Explicitly spatial methods of standardization are necessary for analyses of large-scale fossil datasets, because non-spatial sample standardization, such as diversity rarefaction, is insufficient to reduce the signal of varying spatial coverage through time or between environments and clades. Spatial standardization should control both geographic area and dispersion (spread) of fossil localities. In addition to spatial standardization, other factors may be standardized, including environmental heterogeneity or the number of publications or field collecting units that report taxon occurrences. Using a case study of published global Paleobiology Database occurrences, we demonstrate the strong signals of sampling that could be misinterpreted as biologically meaningful, and which spatial standardization accounts for successfully. We discuss practical issues of implementing spatial standardization via subsampling and present the new R package "divvy" to improve the accessibility of spatial analysis. The software provides three spatial subsampling approaches, as well as related tools to quantify spatial coverage. After reviewing the theory, practice, and history of equalizing spatial coverage between data comparison groups, we outline priority areas to improve related data collection, analysis, and reporting practices in paleobiology.