Dealing with under- and over-dispersed count data in life history, spatial, and community ecology

Count data arise frequently in ecological analyses, but regularly violate the equi-dispersion constraint imposed by the most popular distribution for analyzing these data, the Poisson distribution. Several approaches for addressing over-dispersion have been developed (e.g., negative binomial distribution), but methods for including both underdispersion and over-dispersion have been largely overlooked. We provide three specific examples drawn from life-history theory, spatial ecology, and community ecology, and illustrate the use of the Conway-Maxwell-Poisson (CMP) distribution as compared to other common models for count data. We find that where equi-dispersion is violated, the CMP distribution performs significantly better than the Poisson distribution, as assessed by information criteria that account for the CMP's additional distribution parameter. The Conway-Maxwell-Poisson distribution has seen rapid development in other fields such as risk analysis and linguistics, but is relatively unknown in the ecological literature. In addition to providing a more flexible exponential distribution for count data that is easily integrated into generalized linear models, the CMP allows ecologists to focus on the magnitude of under-or over-dispersion as opposed to the simple rejection of the equi-dispersion null hypothesis. By demonstrating its suitability in a variety of common ecological applications, we hope to encourage its wider adoption as a flexible alternative to the Poisson.