Abstract

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform be er than the other commonly used models, for example, ordinary Poisson HGLMs, spa al ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high propor on of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spa al random effects.

Keywords

excessive zeros; habitat preference; hierarchical generalized linear model; pellet-group count; Poisson model; spatial correlation

Published in

Ecology and Evolution
2016, Volume: 6, number: 19, pages: 7047–7056

SLU Authors

• Skarin, Anna

  • Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Animal and Dairy Science
Ecology
Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.1002/ece3.2449

Permanent link to this page (URI)

https://res.slu.se/id/publ/78046