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Applying sequential adaptive strategies for sampling animal populations: An empirical study

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  • Rosa M. Di Biase
  • Fulvia Mecatti

Abstract

Traditional sampling methods may prove inadequate when dealing with spatially clustered populations or when studying rare events or traits that are not easily detectable across the target population. When both scenarios occur simultaneously, adaptive sampling strategies can represent a viable option to enhance the detectability of cases of interest. This paper delves into the application of a novel class of sequential adaptive sampling strategies to animal surveys. These strategies, originally proposed for human population tuberculosis prevalence surveys, allow oversampling of the rare interest variables while managing on‐field constraints. This ensures that the unfixed sample size, typical of adaptive sampling, does not compromise overall cost‐effectiveness. We explore a strategy within this class that integrates an adaptive component into a Poisson sequential selection. The aim is twofold: to intensify the detection of cases by exploiting the spatial clustering and to provide a flexible framework for managing logistics and budget constraints. To illustrate the strengths and weaknesses of this Poisson‐based sequential adaptive sampling strategy compared to traditional sampling methods, a simulation study was conducted on a blue‐winged teal population in Florida, USA. The results showcase the benefits of the proposed strategy and open avenues for future methodological and practical improvements.

Suggested Citation

  • Rosa M. Di Biase & Fulvia Mecatti, 2025. "Applying sequential adaptive strategies for sampling animal populations: An empirical study," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.
  • Handle: RePEc:wly:envmet:v:36:y:2025:i:1:n:e2870
    DOI: 10.1002/env.2870
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    1. Maria Michela Dickson & Yves Tillé, 2016. "Ordered spatial sampling by means of the traveling salesman problem," Computational Statistics, Springer, vol. 31(4), pages 1359-1372, December.
    2. Fulvia Mecatti & Charalambos Sismanidis & Emanuela Furfaro & Pier Luigi Conti, 2023. "Sequential adaptive strategies for sampling rare clustered populations," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1659-1693, December.
    3. Stefano Gattone & Tonio Di Battista, 2011. "Adaptive cluster sampling with a data driven stopping rule," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 1-21, March.
    4. Stevens, Don L. & Olsen, Anthony R., 2004. "Spatially Balanced Sampling of Natural Resources," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 262-278, January.
    5. Steven K. Thompson, 2006. "Adaptive Web Sampling," Biometrics, The International Biometric Society, vol. 62(4), pages 1224-1234, December.
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