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Accuracy or precision: Implications of sample design and methodology on abundance estimation

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  • Kowalewski, Lucas K.
  • Chizinski, Christopher J.
  • Powell, Larkin A.
  • Pope, Kevin L.
  • Pegg, Mark A.

Abstract

Sampling by spatially replicated counts (point-count) is an increasingly popular method of estimating population size of organisms. Challenges exist when sampling by point-count method, and it is often impractical to sample entire area of interest and impossible to detect every individual present. Ecologists encounter logistical limitations that force them to sample either few large-sample units or many small sample-units, introducing biases to sample counts. We generated a computer environment and simulated sampling scenarios to test the role of number of samples, sample unit area, number of organisms, and distribution of organisms in the estimation of population sizes using N-mixture models. Many sample units of small area provided estimates that were consistently closer to true abundance than sample scenarios with few sample units of large area. However, sample scenarios with few sample units of large area provided more precise abundance estimates than abundance estimates derived from sample scenarios with many sample units of small area. It is important to consider accuracy and precision of abundance estimates during the sample design process with study goals and objectives fully recognized, although and with consequence, consideration of accuracy and precision of abundance estimates is often an afterthought that occurs during the data analysis process.

Suggested Citation

  • Kowalewski, Lucas K. & Chizinski, Christopher J. & Powell, Larkin A. & Pope, Kevin L. & Pegg, Mark A., 2015. "Accuracy or precision: Implications of sample design and methodology on abundance estimation," Ecological Modelling, Elsevier, vol. 316(C), pages 185-190.
    • Handle: RePEc:eee:ecomod:v:316:y:2015:i:c:p:185-190
    DOI: 10.1016/j.ecolmodel.2015.08.016
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    References listed on IDEAS

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    1. Robert M. Dorazio & J. Andrew Royle, 2003. "Mixture Models for Estimating the Size of a Closed Population When Capture Rates Vary among Individuals," Biometrics, The International Biometric Society, vol. 59(2), pages 351-364, June.
    2. J. Andrew Royle, 2004. "N-Mixture Models for Estimating Population Size from Spatially Replicated Counts," Biometrics, The International Biometric Society, vol. 60(1), pages 108-115, March.
    3. Fiske, Ian & Chandler, Richard, 2011. "unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i10).
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