Sampling Strategies to Estimate Deer Density by Drive Counts

Lorenzo Fattorini (), Alberto Meriggi, Enrico Merli and Paolo Varuzza
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Lorenzo Fattorini: University of Siena
Alberto Meriggi: University of Pavia
Enrico Merli: Agriculture and Wildlife Service, Regione Emilia – Romagna
Paolo Varuzza: Geographica srl

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 2, No 3, 168-185

Abstract: 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.

Keywords: Design-based inference; Horvitz–Thompson estimator; Monte Carlo simulations; Ratio estimator; Spatial sampling (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s13253-020-00386-3

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