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An Application of Spatio-Temporal Modeling to Finite Population Abundance Prediction.
- Source :
- Journal of Agricultural, Biological & Environmental Statistics (JABES); Sep2024, Vol. 29 Issue 3, p491-515, 25p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ENVIRONMENTAL monitoring
ECOLOGICAL surveys
MOOSE
ACQUISITION of data
ECOLOGISTS
Subjects
Details
- Language :
- English
- ISSN :
- 10857117
- Volume :
- 29
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Journal of Agricultural, Biological & Environmental Statistics (JABES)
- Publication Type :
- Academic Journal
- Accession number :
- 178855258
- Full Text :
- https://doi.org/10.1007/s13253-023-00565-y