%0 Journal Article %1 martin_zero_2005 %A Martin, Tara G. %A Wintle, Brendan A. %A Rhodes, Jonathan R. %A Kuhnert, Petra M. %A Field, Scott A. %A Low-Choy, Samantha J. %A Tyre, Andrew J. %A Possingham, Hugh P. %D 2005 %J Ecology Letters %K Bayesian Poisson, binomial, detectability, error, false inference, inflation inflation, mixture model, negative, observation sampling zero zero-inflated %N 11 %P 1235--1246 %R 10.1111/j.1461-0248.2005.00826.x %T Zero tolerance ecology: improving ecological inference by modelling the source of zero observations %U http://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2005.00826.x/abstract %V 8 %X 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.

@article{martin_zero_2005, 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.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Martin, Tara G. and Wintle, Brendan A. and Rhodes, Jonathan R. and Kuhnert, Petra M. and Field, Scott A. and Low-Choy, Samantha J. and Tyre, Andrew J. and Possingham, Hugh P.}, biburl = {https://www.bibsonomy.org/bibtex/29a1d8dd467de8ec8b6656f0a1d90ec6d/yourwelcome}, doi = {10.1111/j.1461-0248.2005.00826.x}, interhash = {55a2aee990bb38fc762262aec2d3723a}, intrahash = {9a1d8dd467de8ec8b6656f0a1d90ec6d}, issn = {1461-0248}, journal = {Ecology Letters}, keywords = {Bayesian Poisson, binomial, detectability, error, false inference, inflation inflation, mixture model, negative, observation sampling zero zero-inflated}, language = {en}, number = 11, pages = {1235--1246}, shorttitle = {Zero tolerance ecology}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Zero tolerance ecology: improving ecological inference by modelling the source of zero observations}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2005.00826.x/abstract}, urldate = {2012-09-18}, volume = 8, year = 2005 }