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East African Journal of Science and Technology is indexed in International Scientific Indexing (ISI). The Journal has Impact Factor Value of 2.671 based on International Citation Report (ICR) for the year 2023-2024.

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A review of Non-Parametric and Parametric Models for Species Richness Estimation.

Authors

  • Evans Otieno Ochiaga Phastar Limited Company, Nairobi, Kenya
  • Frederic Ntirenganya University of Lay Adventists of Kigali (UNILAK), Kigali, Rwanda

DOI:

https://doi.org/10.62103/unilak.eajst.10.10.172

Keywords:

Non-parametric and parametric models, species richness

Abstract

Species richness estimation is one of the key concepts in conservation biology. Many models have been developed to estimate species richness: ranging from commonly used non-parametric to parametric models. However, not all the models give excellent prediction of number of species in the community. Therefore, in this paper we present and compare the performances of 5 commonly used non-parametric and 9 parametric models. In this research we use Barro Colorado Island (BCI) dataset as the assemblage with 10%, 5%, 2%, and 1% of individuals being drawn for the estimations. The overall performances of the models were done using Akaike Information Criterion variances at 100 simulations. Five non-parametric models underestimate the species richness and nine parametric models overestimate the species richness. Among all the models, abundance coverage estimate model performed the best.

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Published

2020-12-18

How to Cite

Evans Otieno Ochiaga, & Frederic Ntirenganya. (2020). A review of Non-Parametric and Parametric Models for Species Richness Estimation. East African Journal of Science and Technology, 10(10). https://doi.org/10.62103/unilak.eajst.10.10.172