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  5. A novel method for quantifying overdispersion in count data with application to farmland birds
 
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A novel method for quantifying overdispersion in count data with application to farmland birds

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Alternative Title
Quantifying overdispersion in bird data
Author(s)
McMahon, Barry J. 
Purvis, Gordon 
Sheridan, Helen 
Siriwardena, Gavin M. 
Parnell, Andrew C. 
Uri
http://hdl.handle.net/10197/9383
Date Issued
April 2017
Date Available
17T12:08:11Z May 2018
Abstract
The statistical modelling of count data permeates the discipline of ecology. Such data often exhibit overdispersion compared with a standard Poisson distribution, so that the variance of the counts is greater than that of the mean. Whereas modelling to reveal the effects of explanatory variables on the mean is commonplace, overdispersion is generally regarded as a nuisance parameter to be accounted for and subsequently ignored. Instead, we propose a method that models the overdispersion as a biologically interesting property of a data set and show how novel inference is provided as a result. We adapted the double hierarchical generalized linear model approach to create an easily extendible model structure that quantifies the influence of explanatory variables on the overdispersion of count data, and apply it to farmland birds. These data were from a study within Irish agricultural ecosystems, in which total bird species abundance and the abundance of farmland indicator species were compared on dairy and non-dairy farms in the winter and breeding seasons. In general, overdispersion in bird counts was greater on dairy farms than on non-dairy farms, and for total bird numbers, overdispersion was greatest on dairy farms in winter. Our code is fitted using the Bayesian package Rstan, and we make all code and data available in a GitHub repository. Within a Bayesian framework, this approach facilitates a meaningful quantification of the effects of categorical explanatory variables on any response variable with a tendency to overdispersion that has a meaningful biological or ecological explanation.
Type of Material
Journal Article
Publisher
Wiley
Journal
Ibis: International Journal of Avian Science
Volume
159
Issue
2
Start Page
406
End Page
414
Copyright (Published Version)
2016 British Ornithologists' Union
Keywords
  • Abundance

  • Agricultural systems

  • Bayesian framework

  • Ecological data

DOI
10.1111/ibi.12450
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Agriculture and Food Science Research Collection
Scopus© citations
2
Acquisition Date
Jan 25, 2023
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Views
1053
Acquisition Date
Jan 26, 2023
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Downloads
203
Acquisition Date
Jan 26, 2023
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