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Estimating global species richness using symbolic data meta-analysis

Huan-xiang Lin,M. Caley,S. Sisson

Published 2017 in arXiv: Applications

ABSTRACT

Global species richness is a key biodiversity metric. Despite recent efforts to estimate global species richness, the resulting estimates have been highly uncertain and often logically inconsistent. Estimates lower down either the taxonomic or geographic hierarchies are often larger than those above. Further, these estimates have been represented in a wide variety of forms, including intervals (a, b), point estimates with no uncertainty, and point estimates with either symmetrical or asymmetrical bounds, making it difficult to combine information across different estimates. Here, we develop a Bayesian hierarchical approach to estimate the global species richness from published studies. It allows us to recover interval estimates at each level of the hierarchy, even when data are partially or wholly unobserved, while respecting logical constraints, and to determine the effects of estimation on the whole hierarchy of obtaining future estimates anywhere within it

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv: Applications

  • Publication date

    2017-11-08

  • Fields of study

    Biology, Mathematics, Environmental Science

  • Identifiers
    arXiv 1711.03202
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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