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Sampling Strategies to Estimate Deer Density by Drive Counts

Author

Listed:
  • Lorenzo Fattorini

    (University of Siena)

  • Alberto Meriggi

    (University of Pavia)

  • Enrico Merli

    (Agriculture and Wildlife Service, Regione Emilia – Romagna)

  • Paolo Varuzza

    (Geographica srl)

Abstract

The best evaluation of deer density can be achieved by accurate drive counts of deer performed in all the suitable wooded patches of the area of interest. This would provide the true density within drive areas which, in turn, should be akin to the true density within the study area. Because the drive of all these areas is prohibitive, only a subset is usually driven. Results are highly dependent on the subjective choice of the areas. In the present study, an objective design-based approach is considered to select the areas to be driven according to some probabilistic sampling schemes, and deer density in the whole collection of drive areas is estimated by means of some criteria. The schemes should be able to achieve samples of areas evenly spread onto the study region. The criteria should be able to exploit the information provided by the area sizes. Four sampling strategies are considered, together with methods to estimate their precision. They are evaluated by means of a simulation study performed on artificial and real populations. Results from artificial populations determine the best strategies to be used. Results from real populations show that precise estimates are achieved at the cost of sampling 20% of the drive areas. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Lorenzo Fattorini & Alberto Meriggi & Enrico Merli & Paolo Varuzza, 2020. "Sampling Strategies to Estimate Deer Density by Drive Counts," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 168-185, June.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:2:d:10.1007_s13253-020-00386-3
    DOI: 10.1007/s13253-020-00386-3
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    References listed on IDEAS

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    1. Anton Grafström & Niklas L. P. Lundström & Lina Schelin, 2012. "Spatially Balanced Sampling through the Pivotal Method," Biometrics, The International Biometric Society, vol. 68(2), pages 514-520, June.
    2. Lorenzo Fattorini, 2006. "Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities," Biometrika, Biometrika Trust, vol. 93(2), pages 269-278, June.
    3. Jean-Claude Deville & Yves Tille, 2004. "Efficient balanced sampling: The cube method," Biometrika, Biometrika Trust, vol. 91(4), pages 893-912, December.
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