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An Application of Spatio-Temporal Modeling to Finite Population Abundance Prediction

Author

Listed:
  • Matt Higham

    (St. Lawrence University)

  • Michael Dumelle

    (United States Environmental Protection Agency)

  • Carly Hammond

    (Alaska Department of Fish and Game)

  • Jay Hoef

    (National Oceanic and Atmospheric Administration)

  • Jeff Wells

    (Alaska Department of Fish and Game)

Abstract

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be insufficient resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Matt Higham & Michael Dumelle & Carly Hammond & Jay Hoef & Jeff Wells, 2024. "An Application of Spatio-Temporal Modeling to Finite Population Abundance Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 491-515, September.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:3:d:10.1007_s13253-023-00565-y
    DOI: 10.1007/s13253-023-00565-y
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    References listed on IDEAS

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    1. Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
    2. Cressie, N. & Lahiri, S. N., 1993. "The Asymptotic Distribution of REML Estimators," Journal of Multivariate Analysis, Elsevier, vol. 45(2), pages 217-233, May.
    3. Heyde, C. C., 1994. "A quasi-likelihood approach to the REML estimating equations," Statistics & Probability Letters, Elsevier, vol. 21(5), pages 381-384, December.
    4. Jay Ver Hoef & Josh London & Peter Boveng, 2010. "Fast computing of some generalized linear mixed pseudo-models with temporal autocorrelation," Computational Statistics, Springer, vol. 25(1), pages 39-55, March.
    5. Zhonglei Wang & Zhengyuan Zhu, 2019. "Spatiotemporal Balanced Sampling Design for Longitudinal Area Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 245-263, June.
    6. Michael L. Stein, 2005. "Space-Time Covariance Functions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 310-321, March.
    7. Posa, D., 1993. "A simple description of spatial-temporal processes," Computational Statistics & Data Analysis, Elsevier, vol. 15(4), pages 425-437, May.
    8. Beth E Ross & Mevin B Hooten & David N Koons, 2012. "An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
    9. Lemos, Ricardo T. & Sansó, Bruno, 2009. "A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 5-18.
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