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Optimizing performance of nonparametric species richness estimators under constrained sampling.

Harshana Rajakaruna1, D Andrew R Drake2, Farrah T Chan1

  • 1Great Lakes Laboratory for Fisheries and Aquatic Sciences Fisheries and Oceans Canada Burlington ON Canada.

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Summary

A new method improves species richness estimation by splitting samples, enhancing accuracy for rare species in resource-limited ecological surveys. This technique optimizes sampling efforts and reduces estimation error.

Keywords:
ChaoJackknifeabundance‐based estimatorbiodiversity statisticscommunity ecologyincidence‐based estimatorrare species

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Area of Science:

  • Ecology
  • Biodiversity Science
  • Statistical Ecology

Background:

  • Accurate species richness estimation is crucial for ecological research and conservation.
  • Optimizing limited sampling resources against estimation error requires understanding sample size effects on estimators.
  • Nonparametric estimators like Chao and Jackknife show promise but their performance under constrained sampling varies.

Purpose of the Study:

  • To explore and evaluate a novel method for improving species richness estimators under constrained sampling scenarios.
  • To compare the performance of abundance-based estimators with single samples versus incidence-based estimators with split samples.
  • To identify which incidence-based estimator performs best under different species-abundance distribution (SAD) conditions.

Main Methods:

  • A method of randomly splitting species-abundance data from a single sample into two equal halves was proposed.
  • Incidence-based estimators were applied to the split samples to estimate species richness.
  • Monte Carlo Markov Chain (MCMC) simulations assuming lognormal SADs with varying coefficients of variation (CV) were used to evaluate estimator performance based on expected mean-squared error.
  • The method was tested for Chao, Jackknife, ICE, and ACE estimators.
  • The splitting method's effectiveness was also assessed for log series, geometric series, and negative binomial SADs.

Main Results:

  • The proposed splitting method substantially increased the performance of species richness estimators.
  • Chao2 performed best when the coefficient of variation (CV) was less than 0.65.
  • Incidence-based Jackknife performed best when CV was greater than 0.65, provided sample size exceeded a critical value.
  • The method was more effective when estimating richness in assemblages with a higher proportion of rare species.
  • The splitting method demonstrated qualitative similarity across different SADs.

Conclusions:

  • The proposed sample-splitting method offers a valuable alternative to sampling more individuals for improving richness estimation accuracy.
  • This technique is particularly suitable for resource-limited sampling scenarios in ecological studies.
  • The choice between Chao2 and incidence-based Jackknife depends on the coefficient of variation of the species-abundance distribution.
  • The method enhances the reliability of species richness estimates, aiding biodiversity assessments.