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Lyashevska O, Brus DJ, van der Meer J. Mapping species abundance by a spatial zero-inflated Poisson model: a case study in the Wadden Sea, the Netherlands. Ecol Evol. 2016;6(2):532-43doi: 10.1002/ece3.1880.
Lyashevska, O., Brus, D. J., & van der Meer, J. (2016). Mapping species abundance by a spatial zero-inflated Poisson model: a case study in the Wadden Sea, the Netherlands. Ecology and evolution, 6(2), 532-43. https://doi.org/10.1002/ece3.1880
Lyashevska, Olga, et al. "Mapping species abundance by a spatial zero-inflated Poisson model: a case study in the Wadden Sea, the Netherlands." Ecology and evolution vol. 6,2 (2016): 532-43. doi: https://doi.org/10.1002/ece3.1880
Lyashevska O, Brus DJ, van der Meer J. Mapping species abundance by a spatial zero-inflated Poisson model: a case study in the Wadden Sea, the Netherlands. Ecol Evol. 2016 Jan 09;6(2):532-43. doi: 10.1002/ece3.1880. eCollection 2016 Jan. PMID: 26843936; PMCID: PMC4729254.
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Ecol Evol. 2016 Jan 09;6(2):532-43. doi: 10.1002/ece3.1880. eCollection 2016 Jan.

Mapping species abundance by a spatial zero-inflated Poisson model: a case study in the Wadden Sea, the Netherlands.

Ecology and evolution

Olga Lyashevska, Dick J Brus, Jaap van der Meer

Affiliations

  1. Department of Marine Ecology NIOZ Royal Netherlands Institute for Sea Research P.O. Box 59 1790 AB Den Burg Texel The Netherlands.
  2. Alterra Wageningen University and Research Centre P.O. Box 47 6700AA Wageningen The Netherlands.

PMID: 26843936 PMCID: PMC4729254 DOI: 10.1002/ece3.1880

Abstract

The objective of the study was to provide a general procedure for mapping species abundance when data are zero-inflated and spatially correlated counts. The bivalve species Macoma balthica was observed on a 500×500 m grid in the Dutch part of the Wadden Sea. In total, 66% of the 3451 counts were zeros. A zero-inflated Poisson mixture model was used to relate counts to environmental covariates. Two models were considered, one with relatively fewer covariates (model "small") than the other (model "large"). The models contained two processes: a Bernoulli (species prevalence) and a Poisson (species intensity, when the Bernoulli process predicts presence). The model was used to make predictions for sites where only environmental data are available. Predicted prevalences and intensities show that the model "small" predicts lower mean prevalence and higher mean intensity, than the model "large". Yet, the product of prevalence and intensity, which might be called the unconditional intensity, is very similar. Cross-validation showed that the model "small" performed slightly better, but the difference was small. The proposed methodology might be generally applicable, but is computer intensive.

Keywords: Benthic species; count data; generalized linear spatial modeling; spatial correlation

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