KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases

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Authors Jorge M. Lobo, Joaquín Hortal, José Luís Yela, Andrés Millán, David SánchezFernández , et al.
Journal/Conference Name ECOLOGICAL INDICATORS
Paper Category
Paper Abstract Biodiversity databases are typically incomplete and biased. We identify their three main limitations for characterizing the geographic distributions of species: unknown levels of survey effort, unknown absences of a species from a region, and unknown level of repeated occurrence of a species in different samples collected at the same location. These limitations hinder our ability to distinguish between the actual absence of a species at a given location and its (erroneous) apparent absence as consequence of inadequate surveys. Good practice in biodiversity research requires knowledge of the number, location and degree of completeness of relatively well-surveyed inventories within territorial units. We herein present KnowBR, an application designed to simultaneously estimate the completeness of species inventories across an unlimited number of spatial units and different geographical extents, resolutions and unit expanses from any biodiversity database. We use the number of database records gathered in a territorial unit as a surrogate of survey effort, assuming that such number correlates positively with the probability of recording a species within such area. Consequently, KnowBR uses a “record-by-species” matrix to estimate the relationship between the accumulated number of species and the number of database records to characterize the degree of completeness of the surveys. The final slope of the species accumulation curves and completeness percentages are used to discriminate and map well-surveyed territorial units according to user criteria. The capacity and possibilities of KnowBR are demonstrated through two examples derived from data of varying geographic extent and numbers of records. Further, we identify the main advances that would improve the current functionality of KnowBR.
Date of publication 2018
Code Programming Language R
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