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Zero tolerance ecology: improving ecological inference by modelling the source of zero observations

  1. Tara G. Martin1,*,
  2. Brendan A. Wintle2,
  3. Jonathan R. Rhodes3,
  4. Petra M. Kuhnert4,
  5. Scott A. Field5,
  6. Samantha J. Low-Choy6,
  7. Andrew J. Tyre7,†,
  8. Hugh P. Possingham1

Article first published online: 6 OCT 2005

DOI: 10.1111/j.1461-0248.2005.00826.x

Issue

Ecology Letters

Ecology Letters

Volume 8, Issue 11, pages 1235–1246, November 2005

Additional Information

How to Cite

Martin, T. G., Wintle, B. A., Rhodes, J. R., Kuhnert, P. M., Field, S. A., Low-Choy, S. J., Tyre, A. J. and Possingham, H. P. (2005), Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. Ecology Letters, 8: 1235–1246. doi: 10.1111/j.1461-0248.2005.00826.x

Author Information

  1. 1

    The Ecology Centre, School of Life Sciences, The University of Queensland, Brisbane, Qld 4072, Australia

  2. 2

    Environmental Science, School of Botany, University of Melbourne, Vic. 3010, Australia

  3. 3

    School of Geography, Planning and Architecture, The University of Queensland, Brisbane, Qld 4072, Australia

  4. 4

    CSIRO Mathematical and Information Sciences, Cleveland, Qld, Australia

  5. 5

    School of Earth and Environmental Sciences, University of Adelaide, North Terrace, SA 5005, Australia

  6. 6

    School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld 4001, Australia

  7. 7

    School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA

  1. †

    A contribution of the University of Nebraska Agricultural Research Division, Lincoln, NE 68583, USA. Journal series no. 14657

*Present address: CSIRO Sustainable Ecosystems, QBP, 306 Carmody Road, Qld 4067, Australia. E-mail: tara.martin@csiro.au

Publication History

  1. Issue published online: 6 OCT 2005
  2. Article first published online: 6 OCT 2005
  3. Editor, Marti Anderson Manuscript received 19 May 2005 First decision made 27 June 2005 Second decision made 2 August 2005 Manuscript accepted 16 August 2005

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Keywords:

  • Bayesian inference;
  • detectability;
  • excess zeros;
  • false negative;
  • mixture model;
  • observation error;
  • sampling error;
  • zero-inflated binomial;
  • zero-inflated Poisson;
  • zero inflation

Abstract

A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from ‘true zero’ or ‘false zero’ observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses.

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