MURAL - Maynooth University Research Archive Library



    A novel method for quantifying overdispersion in count data and its application to farmland birds


    Mcmahon, Barry J. and Purvis, Gordon and Sheridan, Helen and Siriwardena, Gavin M. and Parnell, Andrew (2017) A novel method for quantifying overdispersion in count data and its application to farmland birds. International Journal of Avian Science (IBIS), 159 (2). pp. 406-414. ISSN 0019-1019

    [img]
    Preview
    Download (317kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    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.

    Item Type: Article
    Keywords: abundance; agricultural systems; Bayesian framework; ecological data;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 19093
    Identification Number: https://doi.org/10.1111/ibi.12450
    Depositing User: Andrew Parnell
    Date Deposited: 22 Oct 2024 15:51
    Journal or Publication Title: International Journal of Avian Science (IBIS)
    Publisher: Wiley
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads