A general approach to modeling and analysis of species abundance data with extra zeros
A general method for the analysis of ecological count data with extra zeros is presented using a Markov birth process representation of discrete distributions. The method uses a non parametric formulation of the birth process to model the residual variation and therefore allows the data to play a greater role in determining an appropriate distribution. This enables a more critical assessment of covariate effects and more accurate predictions to be made. The approach is also presented as a useful diagnostic tool for suggesting appropriate parametric models or verifying standard models. As an ill ustrative example, data describing a bundance of a species of possum from the montane ash forests of the central highlands of Victoria, southeast Australia, is considered.
Key WordsCovariate effects Extended Poisson process model Penalized likelihood Prediction
- Faddy, M. J. (1998), “Stochastic Models for Analysis of Species Abundance Data,” in Statistics in Ecology and Environmental Monitoring (Vol. 2), eds. D. J. Fletcher, L. Kavalieris, and B. F. J. Manly, Dunedin: University of Otago Press, pp. 33–40.Google Scholar
- Podlich, H. M., Faddy, M. J., and Smyth, G. K. (1999), “Likelihood Computations for Extended Poisson Process Models,” InterStat, September No. 1, 15 pp.Google Scholar