Nonparametric Threshold Model of Zero-Inflated Spatio-Temporal Data with Application to Shifts in Jellyfish Distribution

  • Hai Liu
  • Lorenzo Ciannelli
  • Mary Beth Decker
  • Carol Ladd
  • Kung-Sik Chan


There is increasing scientific interest in studying the spatial distribution of species abundance in relation to environmental variability. Jellyfish in particular have received considerable attention in the literature and media due to regional population increases and abrupt changes in distribution. Jellyfish distribution and abundance data, like many biological datasets, are characterized by an excess of zero counts or nonstationary processes, which hampers their analyses by standard statistical methods. Here we further develop a recently proposed statistical framework, the constrained zero-inflated generalized additive model (COZIGAM), and apply it to a spatio-temporal dataset of jellyfish biomass in the Bering Sea. Our analyses indicate systematic spatial variation in the process that causes the zero inflation. Moreover, we show strong evidence of a range expansion of jellyfish from the southeastern to the northwestern portion of the survey area beginning in 1991. The proposed methodologies could be readily applied to ecological data in which zero inflation and spatio-temporal nonstationarity are suspected, such as data describing species distribution in relation to changes of climate-driven environmental variables. Some supplemental materials including an animation of jellyfish annual biomass and web appendices are available online.

Key Words

Chrysaora melanaster Constraint Generalized additive model Habitat expansion Nonstationary Splines 


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Copyright information

© International Biometric Society 2010

Authors and Affiliations

  • Hai Liu
    • 1
  • Lorenzo Ciannelli
    • 2
  • Mary Beth Decker
    • 3
  • Carol Ladd
    • 4
  • Kung-Sik Chan
    • 5
  1. 1.Division of BiostatisticsIndiana University School of MedicineIndianapolisUSA
  2. 2.College of Oceanic and Atmospheric SciencesOregon State UniversityCorvallisUSA
  3. 3.Department of Ecology and Evolutionary BiologyYale UniversityNew HavenUSA
  4. 4.Pacific Marine Environmental LaboratoryNOAASeattleUSA
  5. 5.Department of Statistics and Actuarial ScienceUniversity of IowaIowa CityUSA

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