Abstract
Generating regional checklists for insects is frequently based on combining data sources ranging from literature and expert assertions that merely imply the existence of an occurrence to aggregated, standard-compliant data of uniquely identified specimens. The increasing diversity of data sources also means that checklist authors are faced with new responsibilities, effectively acting as filterers to select and utilize an expert-validated subset of all available data. Authors are also faced with the technical obstacle to bring more occurrences into Darwin Core-based data aggregation, even if the corresponding specimens belong to external institutions. We illustrate these issues based on a partial update of the Kimsey et al. 2017 checklist of darkling beetles - Tenebrionidae sec. Bousquet et al. 2018 - inhabiting the Algodones Dunes of California. Our update entails 54 species-level concepts for this group and region, of which 31 concepts were found to be represented in three specimen-data aggregator portals, based on our interpretations of the aggregators' data. We reassess the distributions and biogeographic affinities of these species, focusing on taxa that are precinctive (highly geographically restricted) to the Lower Colorado River Valley in the context of recent dune formation from the Colorado River. Throughout, we apply taxonomic concept labels (taxonomic name according to source) to contextualize preferred name usages, but also show that the identification data of aggregated occurrences are very rarely well-contextualized or annotated. Doing so is a pre-requisite for publishing open, dynamic checklist versions that finely accredit incremental expert efforts spent to improve the quality of checklists and aggregated occurrence data.
Highlights
E species, focusing on taxa that are precinctive to the Lower Colorado River Valley in the context of recent dune formation from the Colorado River
Checklist authors who strive to balance taxonomic comprehensiveness with best data science practices face pragmatic choices; in effect acting as filterers of available data sources that range from published literature that merely imply the existence of an occurrence record, to physically vouchered but non-digitized records, to digital records that may lack a uniquely identified physical voucher and to aggregated, fully standard-compliant and, "research-ready" specimens (Seltmann et al 2017)
This paper aims to draw attention to some of the new scientific, technical and social aspects of checklist authorship in a Darwin Core-driven data culture
Summary
Best practices of how to generate species checklists are evolving, because investments into the on-line aggregation of occurrence data (Wieczorek et al 2012, Page et al 2015) are generating new circumstances for creating regional biodiversity checklists (Ferro and Flick 2015, Sikes et al 2016, GBIF 2017). Checklist authors who strive to balance taxonomic comprehensiveness with best data science practices face pragmatic choices; in effect acting as filterers of available data sources that range from published literature that merely imply the existence of an occurrence record, to physically vouchered but non-digitized records, to digital records that may lack a uniquely identified physical voucher and to aggregated, fully standard-compliant and, "research-ready" specimens (Seltmann et al 2017). The latter often represent the most desirable minority of the available data. Our intention is to broaden the discussion of what it means to author highquality checklists when aggregated occurrence data are available
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