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  • Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis

    Author(s)
    Hamilton, Andrew J
    Novotny, Vojtech
    Waters, Edward K
    Basset, Yves
    Benke, Kurt K
    Grimbacher, Peter S
    Miller, Scott E
    Samuelson, G Allan
    Weiblen, George D
    Yen, Jian DL
    Stork, Nigel E
    Griffith University Author(s)
    Stork, Nigel E.
    Year published
    2013
    Metadata
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    Abstract
    Abstract A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction ...
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    Abstract A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical
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    Journal Title
    Oecologia
    Volume
    171
    Issue
    2
    DOI
    https://doi.org/10.1007/s00442-012-2434-5
    Subject
    Ecology
    Terrestrial ecology
    Publication URI
    http://hdl.handle.net/10072/55266
    Collection
    • Journal articles

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