Modeling abundance using N-mixture models: the importance of considering ecological mechanisms

Joseph, Liana N., Elkin, Ché, Martin, Tara G. and Possingham, Hugh P. (2009) Modeling abundance using N-mixture models: the importance of considering ecological mechanisms. Ecological Applications, 19 3: 631-642. doi:10.1890/07-2107.1

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Author Joseph, Liana N.
Elkin, Ché
Martin, Tara G.
Possingham, Hugh P.
Title Modeling abundance using N-mixture models: the importance of considering ecological mechanisms
Journal name Ecological Applications   Check publisher's open access policy
ISSN 1051-0761
Publication date 2009-04
Year available 2009
Sub-type Article (original research)
DOI 10.1890/07-2107.1
Open Access Status File (Publisher version)
Volume 19
Issue 3
Start page 631
End page 642
Total pages 12
Editor David Schimel
Place of publication Tempe, Ariz., United States of America
Publisher Ecological Society of America
Collection year 2010
Language eng
Abstract Predicting abundance across a species' distribution is useful for studies of ecology and biodiversity management. Modeling of survey data in relation to environmental variables can be a powerful method for extrapolating abundances across a species' distribution and, consequently, calculating total abundances and ultimately trends. Research in this area has demonstrated that models of abundance are often unstable and produce spurious estimates, and until recently our ability to remove detection error limited the development of accurate models. The N-mixture model accounts for detection and abundance simultaneously and has been a significant advance in abundance modeling. Case studies that have tested these new models have demonstrated success for some species, but doubt remains over the appropriateness of standard N-mixture models for many species. Here we develop the N-mixture model to accommodate zero-inflated data, a common occurrence in ecology, by employing zero-inflated count models. To our knowledge, this is the first application of this method to modeling count data. We use four variants of the N-mixture model (Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial) to model abundance, occupancy (zero-inflated models only) and detection probability of six birds in South Australia. We assess models by their statistical fit and the ecological realism of the parameter estimates. Specifically, we assess the statistical fit with AIC and assess the ecological realism by comparing the parameter estimates with expected values derived from literature, ecological theory, and expert opinion. We demonstrate that, despite being frequently ranked the “best model” according to AIC, the negative binomial variants of the N-mixture often produce ecologically unrealistic parameter estimates. The zero-inflated Poisson variant is preferable to the negative binomial variants of the N-mixture, as it models an ecological mechanism rather than a statistical phenomenon and generates reasonable parameter estimates. Our results emphasize the need to include ecological reasoning when choosing appropriate models and highlight the dangers of modeling statistical properties of the data. We demonstrate that, to obtain ecologically realistic estimates of abundance, occupancy and detection probability, it is essential to understand the sources of variation in the data and then use this information to choose appropriate error distributions. Copyright ESA. All rights reserved.
Keyword AIC
ecological realism
excess zeros
model choice
negative binomial
Poisson regression
South Australian birds
zero-inflated Poisson
zero inflation
Estimating site occupancy
Q-Index Code C1
Q-Index Status Confirmed Code

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Created: Thu, 03 Sep 2009, 08:29:49 EST by Mr Andrew Martlew on behalf of School of Biological Sciences