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A lattice and random intermediate point sampling design for animal movement

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  • Elizabeth Eisenhauer
  • Ephraim Hanks

Abstract

Animal movement studies have become ubiquitous in animal ecology for the estimation of space use and the analysis of movement behavior. In these studies, animal movement data are primarily collected at regular time intervals. We propose an irregular sampling design that could lead to greater efficiency and information gain in animal movement studies. Our novel sampling design, called lattice and random intermediate point (LARI), combines samples at regular and random time intervals. We compare the LARI sampling design to regular sampling designs in an example with common black carpenter ant location data, an example with guppy location data, and a simulation study of movement with a point of attraction. We modify a general stochastic differential equation model to allow for irregular time intervals and use this framework to compare sampling designs. When parameters are estimated reasonably well, regular sampling results in greater precision and accuracy in prediction of missing data. However, in each of the data and simulation examples explored in this paper, LARI sampling results in more accurate and precise parameter estimation and, thus, better prediction of missing data as well. This result suggests that researchers might gain greater insight into underlying animal movement processes by choosing LARI sampling over regular sampling.

Suggested Citation

  • Elizabeth Eisenhauer & Ephraim Hanks, 2020. "A lattice and random intermediate point sampling design for animal movement," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
  • Handle: RePEc:wly:envmet:v:31:y:2020:i:6:n:e2618
    DOI: 10.1002/env.2618
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    References listed on IDEAS

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    1. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    2. Peter Diggle & Søren Lophaven, 2006. "Bayesian Geostatistical Design," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(1), pages 53-64, March.
    3. James C. Russell & Ephraim M. Hanks & Andreas P. Modlmeier & David P. Hughes, 2017. "Modeling Collective Animal Movement Through Interactions in Behavioral States," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 313-334, September.
    4. Ephraim M. Hanks & Devin S. Johnson & Mevin B. Hooten, 2017. "Reflected Stochastic Differential Equation Models for Constrained Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 353-372, September.
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    Cited by:

    1. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    2. Dhanushi A Wijeyakulasuriya & Elizabeth W Eisenhauer & Benjamin A Shaby & Ephraim M Hanks, 2020. "Machine learning for modeling animal movement," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-30, July.

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