Spatial methods for plot-based sampling of wildlife populations |
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Authors: | Jay M Ver Hoef |
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Institution: | (1) Alaska Department of Fish and Game, 1300 College Road, Fairbanks, AK 99701, USA;(2) National Marine Mammal Laboratory, 7600 Sand Point Way NE, Bldg 4, Seattle, WA 98115-6349, USA |
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Abstract: | Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates
the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version
of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing
mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging.
FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This
method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to
stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1)
FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK
allows nonrandom sampling designs. |
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Keywords: | Geostatistics Variogram Block kriging Finite population BLUP Small area |
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