Negative binomial modelling is one of the most commonly used statistical tools for analysing
count data in ecology and biodiversity research. This is not surprising given the prevalence …

A Poisson process is a commonly used starting point for modeling stochastic variation of
ecological count data around a theoretical expectation. However, data typically show more …

Binomial N‐mixture models are commonly applied to analyse population survey data. By
estimating detection probabilities, N‐mixture models aim at extracting information about …

Quasi‐Poisson and negative binomial regression models have equal numbers of
parameters, and either could be used for overdispersed count data. While they often give …

Overdispersion is a common feature of models of biological data, but researchers often fail
to model the excess variation driving the overdispersion, resulting in biased parameter …

The statistical distributions of the counts of organisms are generally skewed, and hence not
normally distributed, nor are variances constant across treatments. We present a likelihood …

Binomial N‐mixture models have proven very useful in ecology, conservation, and
monitoring: they allow estimation and modeling of abundance separately from detection …

Predicting abundance across a species' distribution is useful for studies of ecology and
biodiversity management. Modeling of survey data in relation to environmental variables can …

Proportional data, in which response variables are expressed as percentages or fractions of
a whole, are analysed in many subfields of ecology and evolution. The scale‐independence …

In many situations we are faced with the need to estimate the number of classes in a
population from observed count data: this arises not only in biology, where we are interested …