A model for analyzing clustered occurrence data. Issue 2 (15th February 2021)

• Record Type: Journal Article
• Authors: Hwang, Wen‐Han; Huggins, Richard; Stoklosa, Jakub
• Is Part Of: Biometrics. Volume 78:Issue 2(2022)
• Page Start: 598
• Page End: 611
• Publication Date: 2021-02-15
• Subjects: composite likelihood -- imperfect detection -- multivariate occurrence model -- negative binomial; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13435
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: Spatial or temporal clustering commonly arises in various biological and ecological applications, for example, species or communities may cluster in groups. In this paper, we develop a new clustered occurrence data model where presence–absence data are modeled under a multivariate negative binomial framework. We account for spatial or temporal clustering by introducing a community parameter in the model that 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 2 or 3 and consider a composite likelihood approach that 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.