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Tree Core Analysis with X-ray Computed Tomography
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A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets.

Neil A White1, Richard M Engeman, Robert T Sugihara

  • 1Department of Primary Industries and Fisheries and Agricultural Production Systems Research Unit, Toowoomba, Queensland, Australia. neil.white@dpi.qld.gov.au

BMC Ecology
|April 18, 2008
PubMed
Summary

Plotless density estimators offer a practical alternative to traditional plot counts for estimating event density. A compound estimator proved robust across various spatial patterns, especially with larger sample sizes.

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Area of Science:

  • Ecology
  • Ecological Statistics

Background:

  • Plotless density estimators use distance measures instead of area counts.
  • Previous studies relied solely on simulated data for error analysis.

Purpose of the Study:

  • Evaluate the robustness of eight plotless density estimators across diverse field data.
  • Assess estimators based on error, bias, calculation ease, and field usability.

Main Methods:

  • Applied Monte Carlo simulations to sample real-world datasets.
  • Evaluated estimators using relative root mean square error.
  • Assessed calculation simplicity and field practicality.

Main Results:

  • A compound estimator combining three basic distance measures was robust across all spatial patterns for sample sizes >= 25.
  • The Kendall-Moran estimator showed reduced error for sample sizes < 25.
  • The variable area transect (VAT) method demonstrated moderate performance and ease of use.

Conclusions:

  • Plotless density estimators are valuable when plot or quadrat sampling is impractical.
  • These methods can significantly reduce fieldwork effort.
  • A specific compound estimator offers robust density estimation across various ecological scenarios.