2005
DOI: 10.1111/j.1461-0248.2005.00826.x
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Zero tolerance ecology: improving ecological inference by modelling the source of zero observations

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 Ôtrue zer… Show more

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Cited by 753 publications
(703 citation statements)
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References 61 publications
(93 reference statements)
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“…Because of a zero-inflated distribution [28], a two-part approach [29,30] was taken to analysing seed set data. First, we used a logistic regression to model the probability of setting seeds (yes or no), with the same predictors as for stigma closure, followed by a LR test.…”
Section: Methods (A) Study Organismsmentioning
confidence: 99%
“…Because of a zero-inflated distribution [28], a two-part approach [29,30] was taken to analysing seed set data. First, we used a logistic regression to model the probability of setting seeds (yes or no), with the same predictors as for stigma closure, followed by a LR test.…”
Section: Methods (A) Study Organismsmentioning
confidence: 99%
“…As with most large food webs, the data display overdispersion with large numbers of zero values (missing interactions), and so a zero-inflated Poisson (ZIP) model is appropriate [29]. In a ZIP model, two generalized linear models are used to explain the data: a logit part for the binary presence-absence of an interaction, and a Poisson part for its magnitude (i.e.…”
Section: Methodsmentioning
confidence: 99%
“…A Poisson distribution was used for this analysis because of the large number of zeros in the data set, and because these zeros are likely to represent true low frequency effects rather than missing data (Martin et al, 2005). The interaction between treatment and location was not significant and was not included as there is insufficient replication in this design (only one treatment and one control plot per block).…”
Section: Statistical Analysesmentioning
confidence: 99%