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  1. The State of Arizona in the south-western United States supports a high diversity of insects. Digitised occurrence records, especially from preserved specimens in natural history collections, are an important and growing resource to understand biodiversity and biogeography. Underlying bias in how insects are collected and what that means for interpreting patterns of insect diversity is largely untested. To explore the effects of insect collecting bias in Arizona, the State was regionalised into specific areas. First, the entire State was divided into broad biogeographic areas by ecoregion. Second, the 81 tallest mountain ranges were mapped on to the State. The distribution of digitised records across these areas were then examined.

    A case study of surveying the beetles (Insecta, Coleoptera) of the Sand Tank Mountains is presented. The Sand Tanks are a low-elevation range in the Lower Colorado River Basin subregion of the Sonoran Desert from which a single beetle record was published before this study.

    The number of occurrence records and collecting events are very unevenly distributed throughout Arizona and do not strongly correlate with the geographic size of areas. Species richness is estimated for regions in Arizona using rarefaction and extrapolation. Digitised records from the disproportionately highly collected areas in Arizona represent at best 70% the total insect diversity within them. We report a total of 141 species of Coleoptera from the Sand Tank Mountains, based on 914 digitised voucher specimens. These specimens add important new records for taxa that were previously unavailable in digitised data and highlight important biogeographic ranges.

    Possible underlying mechanisms causing bias are discussed and recommendations are made for future targeted collecting of under-sampled regions. Insect species diversity is apparently at best 70% documented for the State of Arizona with many thousands of species not yet recorded. The Chiricahua Mountains are the most densely sampled region of Arizona and likely contain at least 2,000 species not yet vouchered in online data. Preliminary estimates for species richness of Arizona are at least 21,000 and likely much higher. Limitations to analyses are discussed which highlight the strong need for more insect occurrence data.

     
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    Free, publicly-accessible full text available June 28, 2024
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    “What is crucial for your ability to communicate with me… pivots on the recipient’s capacity to interpret—to make good inferential sense of the meanings that the declarer is able to send” (Rescher 2000, p148). Conventional approaches to reconciling taxonomic information in biodiversity databases have been based on string matching for unique taxonomic name combinations (Kindt 2020, Norman et al. 2020). However, in their original context, these names pertain to specific usages or taxonomic concepts, which can subsequently vary for the same name as applied by different authors. Name-based synonym matching is a helpful first step (Guala 2016, Correia et al. 2018), but may still leave considerable ambiguity regarding proper usage (Fig. 1). Therefore, developing "taxonomic intelligence" is the bioinformatic challenge to adequately represent, and subsequently propagate, this complex name/usage interaction across trusted biodiversity data networks. How do we ensure that senders and recipients of biodiversity data not only can share messages but do so with “good inferential sense” of their respective meanings? Key obstacles have involved dealing with the complexity of taxonomic name/usage modifications through time, both in terms of accounting for and digitally representing the long histories of taxonomic change in most lineages. An important critique of proposals to use name-to-usage relationships for data aggregation has been the difficulty of scaling them up to reach comprehensive coverage, in contrast to name-based global taxonomic hierarchies (Bisby 2011). The Linnaean system of nomenclature has some unfortunate design limitations in this regard, in that taxonomic names are not unique identifiers, their meanings may change over time, and the names as a string of characters do not encode their proper usage, i.e., the name “Genus species” does not specify a source defining how to use the name correctly (Remsen 2016, Sterner and Franz 2017). In practice, many people provide taxonomic names in their datasets or publications but not a source specifying a usage. The information needed to map the relationships between names and usages in taxonomic monographs or revisions is typically not presented it in a machine-readable format. New approaches are making progress on these obstacles. Theoretical advances in the representation of taxonomic intelligence have made it increasingly possible to implement efficient querying and reasoning methods on name-usage relationships (Chen et al. 2014, Chawuthai et al. 2016, Franz et al. 2015). Perhaps most importantly, growing efforts to produce name-usage mappings on a medium scale by data providers and taxonomic authorities suggest an all-or-nothing approach is not required. Multiple high-profile biodiversity databases have implemented internal tools for explicitly tracking conflicting or dynamic taxonomic classifications, including eBird using concept relationships from AviBase (Lepage et al. 2014); NatureServe in its Biotics database; iNaturalist using its taxon framework (Loarie 2020); and the UNITE database for fungi (Nilsson et al. 2019). Other ongoing projects incorporating taxonomic intelligence include the Flora of Alaska (Flora of Alaska 2020), the Mammal Diversity Database (Mammal Diversity Database 2020) and PollardBase for butterfly population monitoring (Campbell et al. 2020). 
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  4. This contribution adopts the taxonomic concept approach, including the use oftaxonomic concept labels(name sec. [according to] source) and region connection calculus-5 (RCC–5) articulations and alignments. Prior to this study, the broad-nosed weevil genusMinyomerusHorn, 1876 sec. Jansen & Franz, 2015 (Curculionidae [non-focal]: Entiminae [non-focal]: Tanymecini [non-focal]) contained 17 species distributed throughout the desert and plains regions of North America. In this review ofMinyomerussec. Jansen & Franz, 2018, we describe the following four species as new to science:Minyomerus ampullaceussec. Jansen & Franz, 2018 (henceforth: [JF2018]), new species,Minyomerus franko[JF2018], new species,Minyomerus sculptilis[JF2018], new species, andMinyomerus tylotos[JF2018], new species. The four new species are added to, and integrated with, the preceding revision, and an updated key and phylogeny ofMinyomerus[JF2018] are presented. A cladistic analysis using 52 morphological characters of 26 terminal taxa (5/21 outgroup/ingroup) yielded a single most-parsimonious cladogram (Length = 99 steps, consistency index = 60, retention index = 80). The analysis reaffirms the monophyly ofMinyomerus[JF2018] with eight unreversed synapomorphies. The species-group placements, possible biogeographic origins, and natural history of the new species are discussed in detail.

     
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  5. 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. 
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