SpringerLink
Log in

Dynamic spatio-temporal zero-inflated Poisson models for predicting capelin distribution in the Barents Sea

  • Original Paper
  • Published:
Japanese Journal of Statistics and Data Science Aims and scope Submit manuscript

Abstract

We consider modeling and prediction of Capelin distribution in the Barents Sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixing proportions are modeled by some auxiliary variables and unobserved spatio-temporal effects. The spatio-temporal effects are modeled by a dynamic linear model combined with the predictive Gaussian process. We develop an efficient posterior computational algorithm for the model using a data augmentation strategy. The performance of the proposed model is demonstrated through simulation studies, and an application to the number of Capelin caught in the Barents Sea from 2014 to 2019.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC Press.

    Book  MATH  Google Scholar 

  • Banerjee, S., Gelfand, A. E., Finley, A. O., & Sang, H. (2008). Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(4), 825–848.

    Article  MathSciNet  MATH  Google Scholar 

  • Bradley, J. R., Holan, S. H., & Wikle, C. K. (2018). Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion). Bayesian Analysis, 13, 253–310.

    Article  MathSciNet  MATH  Google Scholar 

  • Bradley, J. R., Holan, S. H., & Wikle, C. K. (2020). Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family. Journal of the American Statistical Association, 115(532), 2037–2052.

    Article  MathSciNet  MATH  Google Scholar 

  • Carson, S., Shackell, N., & Mills Flemming, J. (2017). Local overfishing may be avoided by examining parameters of a spatio-temporal model. PLoS ONE, 12(9), e0184427.

    Article  Google Scholar 

  • Dalpadado, P., Arrigo, K. R., van Dijken, G. L., Skjoldal, H. R., Bagøien, E., Dolgov, A. V., Prokopchuk, I. P., & Sperfeld, E. (2020). Climate effects on temporal and spatial dynamics of phytoplankton and zooplankton in the Barents Sea. Progress in Oceanography, 185, 102320.

    Article  Google Scholar 

  • Datta, A., Banerjee, S., Finley, A. O., & Gelfand, A. E. (2016). Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111(514), 800–812.

    Article  MathSciNet  Google Scholar 

  • Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350.

    MathSciNet  MATH  Google Scholar 

  • Fall, J., Ciannelli, L., Skaret, G., & Johannesen, E. (2018). Seasonal dynamics of spatial distributions and overlap between Northeast Arctic cod (Gadus morhua) and capelin (Mallotus villosus) in the Barents Sea. PLoS ONE, 13(10), 102320.

    Article  Google Scholar 

  • Gelfand, A. E., & Ghosh, S. K. (1998). Model choice: A minimum posterior predictive loss approach. Biometrika, 85(1), 1–11.

    Article  MathSciNet  MATH  Google Scholar 

  • Ghosal, S., Lau, T. S., Gaskins, J., & Kong, M. (2020). A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(5), 1121–1144.

    MathSciNet  Google Scholar 

  • Gjøsæter, H., Bogstad, B., & Tjelmeland, S. (2002). Assessment methodology for Barents Sea capelin, Mallotus villosus (Müller). ICES Journal of Marine Science, 59(5), 1086–1095.

    Article  Google Scholar 

  • Glynn, C., Tokdar, S. T., Howard, B., & Banks, B. L. (2019). Bayesian analysis of dynamic linear topic models. Bayesian Analysis, 14, 53–80.

    Article  MathSciNet  MATH  Google Scholar 

  • Hamura, Y., Irie, K., & Sugasawa, S. (2021). Robust hierarchical modeling of counts under zero-inflation and outliers. Preprint ar**v:2106.10503.

  • Ingvaldsen, R. B., & Gjøsæter, H. (2013). Responses in spatial distribution of Barents Sea capelin to changes in stock size, ocean temperature and ice cover. Marine Biology Research, 9(9), 867–877.

    Article  Google Scholar 

  • Jourdain, N., Fuglebakk, E., & Subbey, S. (2021). Maturation in the Barents sea capelin-contrasting length-and gonad-based metrics. Fisheries Research, 237, 105880.

    Article  Google Scholar 

  • Kelsall, J., & Wakefield, J. (2002). Modeling spatial variation in disease risk: A geostatistical approach. Journal of the American Statistical Association, 97(459), 692–701.

    Article  MathSciNet  MATH  Google Scholar 

  • Neelon, B. (2018). Bayesian zero-inflated negative binomial regression based on polya-gamma mixtures. Bayesian Analysis, 14, 829.

    MathSciNet  MATH  Google Scholar 

  • Neelon, B., Chang, H. H., Ling, Q., & Hastings, N. S. (2016). Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits. Statistical Methods in Medical Research, 6, 2558–2576.

    Article  MathSciNet  Google Scholar 

  • Polson, N. G., Scott, J. G., & Windle, J. S. (2013). Bayesian inference for logistic models using polya-gamma latent variables. Journal of the American Statistical Association, 108, 1339–1349.

    Article  MathSciNet  MATH  Google Scholar 

  • Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 319–392.

    Article  MathSciNet  MATH  Google Scholar 

  • Stein, M. L. (2014). Limitations on low rank approximations for covariance matrices of spatial data. Spatial Statistics, 8, 1–19.

    Article  MathSciNet  Google Scholar 

  • Torabi, M. (2017). Zero-inflated spatio-temporal models for disease map**. Biometrical Journal, 59(3), 430–444.

    Article  MathSciNet  MATH  Google Scholar 

  • Ver Hoef, J. M., & Jansen, J. K. (2007). Space-time zero-inflated count models of harbor seals. Environmetrics: The Official Journal of the International Environmetrics Society, 18(7), 697–712.

    Article  MathSciNet  Google Scholar 

  • Wang, X., Chen, M., Kuo, R. C., & Dey, D. K. (2015). Bayesian spatial–temporal modeling of ecological zero-inflated count data. Statistica Sinica, 25, 189–204.

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

S. Sugasawa is supported by Japan Society for Promotion of Science (KAKENHI) under Grant Nos. 18H03628, 20H00080, and 21H00699. T. Nakagawa is supported by Japan Society for Promotion of Science (KAKENHI) under Grant Nos. 19K14597 and 21H00699. S. Subbey and H. K. Solvang have been supported by Grants 84126—Management Strategy for the Barents Sea, and GA19-NOR-081 from the Sasakawa foundation (Norway).

Author information

Authors and Affiliations

  1. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8568, Japan

    Shonosuke Sugasawa

  2. Department of Information Sciences, Tokyo University of Science, Chiba, Japan

    Tomoyuki Nakagawa

  3. Marine Mammals Research Group, Institute of Marine Research, Bergen, Norway

    Hiroko Kato Solvang

  4. Research Group on Fisheries Dynamics, Institute of Marine Research, Bergen, Norway

    Sam Subbey & Salah Alrabeei

  5. Western Norway University of Applied Sciences, Bergen, Norway

    Sam Subbey & Salah Alrabeei

Authors
  1. Shonosuke Sugasawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomoyuki Nakagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiroko Kato Solvang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sam Subbey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Salah Alrabeei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shonosuke Sugasawa.

Ethics declarations

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sugasawa, S., Nakagawa, T., Solvang, H.K. et al. Dynamic spatio-temporal zero-inflated Poisson models for predicting capelin distribution in the Barents Sea. Jpn J Stat Data Sci 6, 1–20 (2023). https://doi.org/10.1007/s42081-022-00183-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42081-022-00183-x

Keywords

Search

Navigation