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

Andrew J. Hamilton, Vojtech Novotný, Edward K. Waters, Yves Basset, Kurt K. Benke, Peter S. Grimbacher, Scott E. Miller, G. Allan Samuelson, George D. Weiblen, Jian D.L. Yen, Nigel E. Stork

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38 Scopus citations

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 independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0. 5 cumulative probability (i. e., at the median estimate) of 2. 9-12. 7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2. 4-20. 0 million at 0. 5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i. e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades.

Original languageEnglish (US)
Pages (from-to)357-365
Number of pages9
JournalOecologia
Volume171
Issue number2
DOIs
StatePublished - Feb 2013

Bibliographical note

Funding Information:
Acknowledgments Cindy Hauser provided useful technical comments on a draft of this manuscript. The host specificity studies in New Guinea, upon which this model draws substantially, were supported by the National Science Foundation (USA) (DEB-0841885), Christensen Fund (USA), Grant Agency of the Czech Republic (206/09/0115), Czech Academy of Sciences, the Swedish Natural Science Research Council, Czech Ministry of Education (CZ.1.07/2.3.00/20.0064, LH11008), Otto Kinne Foundation, Darwin Initiative (UK) (19-008), International Centre of Insect Physiology and Ecology (ICIPE) and Bishop Museum. Parataxonomists in New Guinea are thanked for their assistance and are listed in Novotny´ et al. (2002). This paper is dedicated to the late Ken Hamilton, the consummate logician and giver.

Keywords

  • Host specificity
  • Model
  • Monte Carlo
  • Uncertainty

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