Improving precision and reducing bias in biological surveys: estimating false-negative error rates

Tyre, A. J., Tenhumberg, B., Field, S. A., Niejalke, D., Parris, K. and Possingham, H. P. (2003) Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecological Applications, 13 6: 1790-1801. doi:10.1890/02-5078

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Author Tyre, A. J.
Tenhumberg, B.
Field, S. A.
Niejalke, D.
Parris, K.
Possingham, H. P.
Title Improving precision and reducing bias in biological surveys: estimating false-negative error rates
Journal name Ecological Applications   Check publisher's open access policy
ISSN 1051-0761
Publication date 2003
Sub-type Article (original research)
DOI 10.1890/02-5078
Open Access Status File (Publisher version)
Volume 13
Issue 6
Start page 1790
End page 1801
Total pages 12
Editor L. F. Pitelka
Place of publication Washington, DC, United States
Publisher The Ecological Society of America
Collection year 2004
Language eng
Abstract The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of error associated with these data. We show that false-negative errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of false-negative errors and the correction of estimates of the probability of occurrence for false-negative errors by using repeated. visits to the same site. Our simulations show that even relatively low rates of false negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of false-negative errors In general, when error rates are less than or equal to50% greater efficiency is gained by adding more sites, whereas when error rates are >50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of false-negative errors for a range of commonly used survey methods.
Keyword Biological Surveys
False-negative Errors
Habitat Effects
Presence-absence Data
Zero-inflated Binomial (zib) Model
Small Ground Vertebrates
Pitfall Capture Rates
Arid South-australia
Habitat Requirements
Eucalypt Forests
Greater Glider
Q-Index Code C1

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Created: Wed, 15 Aug 2007, 03:49:23 EST