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

Content Provider	Springer Nature Link
Author	Hamilton, Andrew J. votný, Vojtech Waters, Edward K. Basset, Yves Benke, Kurt K. Grimbacher, Peter S. Miller, Scott E. Samuelson, G. Allan Weiblen, George D. Yen, Jian D. L. Stork, Nigel E.
Copyright Year	2012
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.
Starting Page	357
Ending Page	365
Page Count	9
File Format	PDF
ISSN	00298549
Journal	Oecologia
Volume Number	171
Issue Number	2
e-ISSN	14321939
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-09-12
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Host specificity Model Monte Carlo Uncertainty Ecology Plant Sciences
Content Type	Text
Resource Type	Article
Subject	Ecology, Evolution, Behavior and Systematics

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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