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

By Tara G. Martin, Brendan A. Wintle, J. Rhodes, Petra M. Kuhnert, S. Field, S. Low-Choy, Andrew J. Tyre, Hugh P. Possingham. Ecology Letters, 2005

969 citations recorded by the source corpus.

Subfields: Ecological Modeling, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0b21962d5d243b4aa5cc7c60ed728fd75ce8e9ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Economics and Econometrics

In Nature and Landscape Conservation

Citations in the corpus

Climate‐based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna (2009, background): “This can bias the parameter estimates of statistical models (Martin et al., 2005).”
Expanding northward: influence of climate change, forest connectivity, and population processes on a threatened species' range shift (2011, methodology): “However, zero-inflated Poisson regression (ZIP, Lambert, 1992) can be better suited than Poisson regression for data that have a large proportion of zeroes (Martin et al., 2005).”
For testing the significance of regression coefficients, go ahead and log‐transform count data (2015, background): “GLMs and GLMMs make explicit assumptions about the distribution of the data, and if these assumptions are not met by the data, the statistical results could be quite wrong (e.g. Martin et al. 2005; O’Hara 2005; Ver Hoef & Boveng 2007).”
Changing philosophies and tools for statistical inferences in behavioral ecology (2009, methodology): “Data transformations in these cases do not work for normalization, so it is required to employ special statistical methods to analyze zero-inflated data (Martin et al. 2005).”
Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring (2016, methodology): “…( C 5 0) is identical to a Poisson distribution with intensity m p ; 2 k p s ; t ð Þ : We here use the same linear prediction k p ( s , t ) to control positive catch rates and the probability of encounter, but future research could explore more detailed models for the latter (Martin et al ., 2005).”
Model‐based inference for estimating shifts in species distribution, area occupied and centre of gravity (2016, methodology): “We use a conventional ‘delta model’ [also known as a ‘hurdle’ model (Martin et al. 2005)] which decomposes density d(s, t) into two components: (i) the probability p(s, t) of encountering the species at a given location and time and (ii) the expected density r(s, t) of the species when…”
Life history and behavioral type in the highly social cichlid Neolamprologus pulcher (2010, background): “How to deal with zero inﬂation in longitudinal data analysis is still debated (e.g., Martin et al. 2005; Min and Agresti 2005).”
Dealing with overdispersed count data in applied ecology (2007, background): “via the estimate for φ) is that the analysis may lead to a greater understanding of ecological mechanisms, which may be important for prediction (Lindsey 1999; Martin et al. 2005).”
Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds (2012, methodology): “Such data are difficult to incorporate into standard parametric models (Martin et al., 2005; Sileshi et al., 2009; Warton, 2005).”
Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework (2010, methodology): “inflated zero counts) can also be modelled in a mechanistic fashion (Martin et al. 2005) and support for the proposed mechanisms quantified using IT-AIC.”
The excess-zero problem in soil animal count data and choice of appropriate models for statistical inference (2008, methodology): “Results of this study agree with the growing body of literature (Welsh et al., 1996; Martin et al., 2005; Warton, 2005; Sileshi, 2006) demonstrating that excess zeros are practical phenomena in count data.”
What does a zero mean? Understanding false, random and structural zeros in ecology (2019, background): “The literature differentiates between true and false zeros (Kuhnert, Martin, Mengersen, & Possingham, 2005; Martin et al., 2005; Zuur, Ieno, Walker, Saveliev, & Smith, 2009).”