Humboldt Core – toward a standardized capture of biological inventories for biodiversity monitoring, modeling and assessment

Species inventories, i.e. the recording of multiple species for a specific place and time, are routinely performed and offer particular value for characterizing biodiversity and its change. However, reporting standards allowing these inventories to be re-used, compared to one another, and further integrated with other sources of biodiversity data are lacking, impeding their broadest utility. Here we provide a conceptual informatics framework for capturing, in a standardized and general way, core information about processes underpinning inventory work. We dub this set of terms Humboldt Core, and demonstrate its utility. This proposed framework is based on a community input process, followed by extensive refinement and testing using published inventories. We first develop a typology of inventories and inventory processes, distinguishing between single, elementary inventories, extended and summary inventories, representing increasing levels of sampling event aggregation. We then further describe typical reporting content for inventory processes, along with their value for inferring absence and use in occupancy modeling. Next we provide an overview of the Humboldt Core terms for capture of geospatial, temporal, taxonomic, and environmental conditions, along with methodological descriptors related to the assessment of sampling effort and inventory completeness. Finally, we introduce a pilot implementation of Humboldt Core for the ingestion and provision of inventory process metadata into Map of Life, demonstrating standardized mobilization of metadata from several hundred previously published summary inventories. Humboldt Core helps facilitate integration of different types of inventories and their use in model-supported assessment of spatial biodiversity and its change, critical for meeting global monitoring goals. Humboldt Core will benefit from further enhancements based on community testing and input, but represents a step toward significantly expanding biodiversity dataset discovery, interoperability, and modeling utility for a type of data essential to the assessment of biodiversity variation in space and time. This article is protected by copyright. All rights reserved.