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Research article - Peer-reviewed, 2016

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

Lee, Youngjo; Alam, Moudud; Noh, Maengseok; Rönnegård, Lars; Skarin, Anna


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.


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