LAUSR

Spatial or temporal clustering commonly arises in various biological and ecological applications e.g., species or communities may cluster in groups. In this paper, we develop a new clustered occurrence data model where presence-absence data are modelled under a multivariate negative binomial framework. We account for spatial or temporal clustering by introducing a community parameter in the model which controls the strength of dependence between observations thereby enhancing the estimation of the mean and dispersion parameters. We provide conditions to show the existence of maximum likelihood estimates when cluster sizes are homogeneous and equal to two or three and consider a composite likelihood approach which allows for additional robustness and flexibility in fitting for clustered occurrence data. The proposed method is evaluated in a simulation study and demonstrated using forest plot data from the Center for Tropical Forest Science. Finally, we present several examples using multiple visit occupancy data to illustrate the difference between the proposed model and those of N-mixture models. This article is protected by copyright. All rights reserved.