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Improving Estimates of Fried’s Index from Mating Competitiveness Experiments

Author

Listed:
  • Dan Pagendam

    (CSIRO Data61, EcoSciences Precinct)

  • Nigel Snoad

    (Verily Life Sciences)

  • Wen-Hsi Yang

    (University of Queensland)

  • Michal Segoli

    (Ben-Gurion University of the Negev)

  • Scott Ritchie

    (James Cook University)

  • Brendan Trewin

    (CSIRO Health and Biosecurity, EcoSciences Precinct)

  • Nigel Beebe

    (CSIRO Health and Biosecurity, EcoSciences Precinct
    University of Queensland)

Abstract

Sterile insect technique (SIT) and incompatible insect technique (IIT) are current methods for biological control of insect populations. Critical to the successful implementation of these biocontrol programs is quantifying the competitiveness of sterile/incompatible male insects for female mates relative to wildtype males. Traditionally, entomologists measure this mating competitiveness through a quantity known as Fried’s Index. We establish that Fried’s Index is mathematically equivalent to the mating competitiveness coefficient that features in many population models used in SIT/IIT programs. Using this insight, we propose a new approach for estimating Fried’s Index from mating competitiveness experiments. We show that this approach offers greater precision and accuracy than the traditional approach that is currently used in many studies. This is demonstrated using both simulation experiments and by analysing real experimental data. To facilitate uptake of the proposed method, we also provide an R package that can be used to easily analyse data from mating competitiveness experiments.

Suggested Citation

  • Dan Pagendam & Nigel Snoad & Wen-Hsi Yang & Michal Segoli & Scott Ritchie & Brendan Trewin & Nigel Beebe, 2018. "Improving Estimates of Fried’s Index from Mating Competitiveness Experiments," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 446-462, December.
  • Handle: RePEc:spr:jagbes:v:23:y:2018:i:4:d:10.1007_s13253-018-0333-x
    DOI: 10.1007/s13253-018-0333-x
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Krisztian Magori & Mathieu Legros & Molly E Puente & Dana A Focks & Thomas W Scott & Alun L Lloyd & Fred Gould, 2009. "Skeeter Buster: A Stochastic, Spatially Explicit Modeling Tool for Studying Aedes aegypti Population Replacement and Population Suppression Strategies," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 3(9), pages 1-18, September.
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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    More about this item

    Keywords

    SIT; IIT; Competition; Aedes; Aegypti; Bayesian;
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