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A Method for Quantifying Foliage-Dwelling Arthropods
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Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis.

Andrew J Hamilton1, Vojtech Novotný, Edward K Waters

  • 1Department of Agriculture and Food Systems, Melbourne School of Land and Environment, The University of Melbourne, Dookie Campus, 940 Dookie-Nalinga Road, Dookie College, VIC, 3647, Australia. andrewjh@unimelb.edu.au

Oecologia
|September 13, 2012
PubMed
Summary

Estimating tropical arthropod species richness is challenging due to model uncertainties. Probability bounds analysis reveals broader estimates, supporting lower species richness figures than previously suggested.

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Area of Science:

  • Ecology
  • Biodiversity Science
  • Statistical Modeling

Background:

  • Estimating tropical arthropod species richness faces significant uncertainty.
  • Previous models largely ignored these uncertainties.
  • Monte Carlo analysis was an initial attempt to address variable uncertainty.

Purpose of the Study:

  • To assess the influence of assumptions on species richness models.
  • To construct probability bounds around existing model predictions.
  • To refine estimates of tropical arthropod species richness.

Main Methods:

  • Employed probability bounds analysis (p-bounds).
  • Investigated assumptions regarding distributional form and variable dependencies.
  • Replaced statistical independence with no dependency assumptions.
  • Utilized probability boxes to represent classes of distributions.

Main Results:

  • The original Monte Carlo model estimated 6.1 million species (90% CI [3.6, 11.4]).
  • P-bounds revealed broad uncertainty due to distributional form and dependencies.
  • No dependency assumptions yielded bounds of 2.9-12.7 million at the median.
  • Probability boxes widened bounds to 2.4-20.0 million at the median.

Conclusions:

  • The refined estimates suggest lower tropical arthropod species richness.
  • The upper bounds did not support extreme hyper-estimates (e.g., 30 million).
  • Probability bounds analysis is crucial for robust biodiversity estimations.